Types Of Agglomerates Research Articles

On the fluidization of cohesive powders: Differences and similarities between micro‐ and nano‐sized particle gas–solid fluidization

AbstractThe fluidization of cohesive powders has been extensively researched over the years. When looking at literature on the fluidization of cohesive particles, one will often find papers concerned with only micro‐ or only nano‐sized powders. It is, however, unclear whether they should be treated differently at all. In this paper, we look at differences and similarities between cohesive powders across the size range of several nanometres to 10s of micrometres. Classification of fluidization behaviour based on particle size was found to be troublesome since cohesive powders form agglomerates and using the properties of these agglomerates introduces new problems. When looking at inter‐particle forces, it is found that van der Waals forces dominate across the entire size range that is considered. Furthermore, when looking into agglomeration and modelling thereof, it was found that there is a fundamental difference between the size ranges in the way they agglomerate. Where the transition between the types of agglomeration is located is, however, unknown. Finally, how models are made and agglomerate sizes are measured is currently insufficient to accurately predict or measure their sizes consistently.

Impact of human capital on the green economy: empirical evidence from 30 Chinese provinces.

In China, exploring the relationship between human capital and green economy in the context of economic transformation can promote the construction of an ecological civilization and high-quality economic development. This paper is dedicated to assessing the impact of human capital stock, human capital structure, and the difference between the two on China's green economy. Using the super-efficiency slack-based measure (SBM) model, this paper evaluated the green total factor productivity (GTFP) of 30 Chinese provinces to assess the greenness of its economy and analyzed its spatiotemporal evolution from 2000 to 2017. A spatial error model (SEM) was established to explore the impact of human capital on green economy. The results show that (1) the GTFP of China increased continuously from 0.219 to 0.457, showing a regional spatial differentiation of "eastern region > central region > western region." The spatial gaps of the green economy in the east-west and north-south directions are narrowing. (2) Moran's I index and Moran's I scatter plot indicate a significant spatial correlation between human capital and China's green economy. The local spatial correlation between human capital and green economy is mainly characterized by "high-high" and "low-low" types of agglomeration. (3) The effects of human capital stock and human capital structure on green economy were both positive, with coefficients of 0.0005 and 0.1601, respectively, but the effects of human capital structure were not significant. (4) The results of regional regression show that the difference between the human capital impact coefficients on green economy in the eastern and midwestern regions is small. The impact of human capital stock and human capital structure on green economy is consistent with the national level results. It can be preliminarily concluded that the development of China's green economy relied more on improving the human capital stock than on the improvement of the human capital structure. This study further enriches the literature on the green economy and provides information that can support government policy-making.

Agricultural water use efficiency and spatial spillover effect considering undesired output in China

Abstract Solving the problem of agricultural water use efficiency is an effective means to understand the agricultural ecological civilization and food security. In order to balance the relationship between agricultural water efficiency and regional economic size, this study examines the agricultural water use efficiency in China based on the Super-SBM model of unexpected output. At the same time, the Spatial Durbin Model (SDM) was introduced to analyze the spatial spillover effect and try to find ways to improve agricultural water use efficiency from the perspective of influencing factors. The present study used the panel data of 30 provinces in China from 1998 to 2018 to obtain empirical results. The results show that (1) China's agricultural water resources’ utilization efficiency in 21 years has not remained high: the results showed that it was 0.496 in 1998, 0.572 in 2008, and 0.657 in 2018, but it is slowly rising which explains that the low efficiency is mainly caused by the low pure technical efficiency. (2) The overall agricultural water use efficiency in China is in a situation of spatial agglomeration but there has been a trend of shifting positive correlations to negative correlations among neighboring provinces. It found that in 1998, the sum of the number of `High-high agglomeration type' and `low-low agglomeration type' provinces was 20, and the sum of `low-high agglomeration type' and `high-low agglomeration type' provinces was 10. In 2018, the total number of `high-high agglomeration type' and `low-low agglomeration type' provinces decreased to 12, while the total number of `low-high agglomeration type' and `high-low agglomeration type' provinces increased to 18, which specifically manifested as a negative spillover effect. (3) During the research period, the province's agricultural fixed asset investment, fiscal expenditure on agriculture, forestry, and water affairs, the number of years of education of rural residents and the increase in crop sown area have positive impacts on the efficiency of agricultural water use in the province. Meanwhile the transfer of labor and increase in the disposable income of rural residents reduced the efficiency of agricultural water use in the province. Fixed agricultural investment, labor transfers, and financial expenditures for agriculture, forestry, and water affairs in neighboring provinces have negative impacts on the province's agricultural water use efficiency. The impact is specifically manifested as a negative spatial spillover effect. As a result, China's agriculture will move towards healthy and green development. Therefore, the efficiency of agricultural water use needs to be comprehensively improved.

Energy consumption in China: Spatial effects of industrial concentration, localization, and diversity

This paper discusses the effects of industrial agglomeration on electrical energy consumption from a spatial perspective by highlighting multiple agglomeration types. It is argued that different types of industry agglomeration may exert differentiated effects on energy consumption and their possible spatial interactions. With city sector panel data of China's 285 prefecture-level cities for the period 2003–2013, we adopted the Spatial Durbin Model (SDM) with fixed effects to examine the spatial effects of industrial agglomeration types on energy consumption. The model found that all types of industrial agglomeration play significant and different roles in explaining overall local energy consumption. Second, geographical proximity, diversity, and related variety show the spatial spillover effects on surrounding areas. Third, the spatial energy effects of industrial agglomeration across regions are evidently heterogeneous. Diversity and one of its sub-forms-related variety maintains consumption-saving effects in regions with the initial or intermediate stage of an industry life cycle, while unrelated variety changes over different phases. The results suggest that policymakers scientifically discriminate regional features and guide the agglomerated types of industries in order to balance economic growth with energy conservation.

Spatial Distribution Pattern and Influencing Factors of Homestays in Chongqing, China

As an emerging business form of tourism development, homestays also play an important role in China’s rural revitalization and tourism transformation and upgrading, and has attracted increasing social attention. At present, Chongqing is the city with the largest number of homestays in China. Taking Chongqing as a case-study area, based on the homestay data of the Baidu map, this paper comprehensively uses the methods of spatial analysis, multiple regression and geographical weighted regression to thoroughly analyze the regional characteristics and influencing factors of homestay distribution in Chongqing. The results show that: (1) the nearest-neighbor index R of homestay distributions in Chongqing and all regions is one, which shows an obvious agglomeration type. (2) In addition to being highly concentrated in the central urban area, three secondary high-density areas are also formed in the surrounding areas of the central urban area, and there is a trend of concentration and contiguity. The spatial distribution densities of the two urban agglomerations in Southeast and Northeast Chongqing are very low, and the overall distributions are extremely uneven. (3) The factors, such as tourism resource endowment, economic development, service industry development, traffic location, consumption demand and social development conditions, have significant impacts on the distribution pattern of homestays, and the impacts of each factor on the layout of homestays has obvious spatial heterogeneity. Analyzing and revealing the temporal and spatial characteristics and dynamic mechanism of homestays has an important theoretical value and practical significance for better serving the new urbanization plan and implementing the strategy of urban–rural integration and rural revitalization.

Direct Imaging of the Atomic Mechanisms Governing the Growth and Shape of Bimetallic Pt-Pd Nanocrystals by In Situ Liquid Cell STEM.

Understanding the atomic mechanisms governing the growth of bimetallic nanoalloys is of great interest for scientists. As a promising material for photocatalysis applications, Pt-Pd bimetallic nanoparticles (NPs) have been in the spotlight for many years due to their catalytic performance, which is typically superior to that of pure Pt NPs. In this work, we use in situ liquid cell scanning transmission electron microscopy to track the exact atomic mechanisms governing the formation of bimetallic Pt-Pd NPs. We find that the formation process of the bimetallic Pt-Pd is divided into three stages. First, the nucleation and growth of ultrasmall primary nanoclusters are formed by the agglomeration of Pt and Pd atoms. Second, the primary nanoclusters are involved in a coalescence process to form two types of bigger agglomerates, namely, amorphous (a-NC) and crystalline (c-NC) nanoclusters. In the third stage, these clusters undergo a coalescence process leading to the formation of Pt-Pd NPs, while, in parallel, monomer attachment continues. We found that the third stage contains three types of coalescence processes, a-NC-a-NC, a-NC-c-NC, and c-NC-c-NC coalescence, which eventually give rise to crystalline bimetallic alloys. However, each type of coalescence gave distinct NPs in terms of shape and defects. Our results thus reveal the exact growth mechanisms of bimetallic alloys on the atomic scale, unravel the origin of their structure, and overall are of key interest to tailor the structure of bimetallic NPs.

What Cause Large Spatiotemporal Differences in Carbon Intensity of Energy-Intensive Industries in China? Evidence from Provincial Data during 2000-2019.

China has been reported as the world’s largest carbon emitter, facing a tough challenge to meet its carbon peaking goal by 2030. Reducing the carbon intensity of energy-intensive industries (EIICI) is a significant starting point for China to achieve its emission reduction targets. To decompose the overall target into regions, understanding the spatiotemporal differences and drivers of carbon intensity is a solid basis for the scientific formulation of differentiated regional emission reduction policies. In this study, the spatiotemporal differences of EIICI are described using the panel data of 30 provinces in China from 2000 to 2019, and a spatial econometric model is further adopted to analyze its drivers. As indicated by the results: (1) from 2000 to 2019, China’s EIICI tended to be reduced continuously, and the spatial differences at the provincial and regional levels expanded continuously, thus revealing the coexistence of “high in the west and low in the east” and “high in the north and low in the south” spatial patterns. (2) There is a significant spatial autocorrelation in the EIICI, characterized by high and high agglomeration and low and low agglomeration types. Moreover, the spatial spillover effects are denoted by a 1% change in the local EIICI, and the adjacent areas will change by 0.484% in the same direction. (3) Technological innovation, energy structure, and industrial agglomeration have direct and indirect effects, thus affecting the local EIICI and the adjacent areas through spatial spillover effects. Economic levels and firm sizes only negatively affect the local EIICI. Environmental regulation merely has a positive effect on adjacent areas. However, the effect of urbanization level on EIICI has not been verified, and the effect of urbanization level on the EIICI has not been verified. The results presented in this study show a scientific insight into the reduction of EIICI in China. Furthermore, policymakers should formulate differentiated abatement policies based on dominant drivers, spatial effects, and regional differences, instead of implementing similar policies in all provinces.

Study on the Spatial-Temporal Evolution of Land Use Ecosystem Service Value and Its Zoning Management and Control in the Typical Alpine Valley Area of Southeast Tibet—Empirical Analysis Based on Panel Data of 97 Villages in Chayu County

Under the background of ecological civilization construction and the overall planning of land and space, it is particularly important to explore the land use ecosystem service value and its zoning control. This paper, taking Chayu County, a typical alpine valley area of southeast Tibet as an example and based on the remote sensing interpretation data of three periods in 2000, 2010 and 2020, employs the three-level spatial scale from the village level, the township level to the county level to converge step by step, and uses a series of model algorithms to analyze and calculate the regional ecosystem service value and their dynamic changes, as well as spatial agglomeration and regional type division. The research shows that the land use types mainly consist of forest land, grassland and unused land, whose overall change range is small during the study period. The conversion of land use types is mainly between forest land, grassland and unused land and the land use index generally presents a spatial pattern of “high in the southwest and low in the northeast”, showing a decreasing trend to some degree. ESVI generally presents a differentiation pattern of “high in the west and low in the east”, with obvious spatial differentiation characteristics of kernel density, significant clustering and distribution characteristics and stable variation range, displaying an overall spatial pattern with characteristics of “dense in the west and sparse in the east, high in the north and low in the south”. Based on the administrative village scale, the study area is divided into three different types of land use ecological function areas: habitat maintenance function area, biological protection function area and production support function area. Differentiated approaches to appropriate development and construction and the corresponding optimization paths of ecological protection will be put forward.

Spatiotemporal heterogeneity and influencing factors on urbanization and eco-environment coupling mechanism in China.

High-quality urbanization is the core for realizing human well-beings, for which reason investigating how the relationship evolves between urbanization and eco-environment is of crucial importance. Differing from the rationale of revealing spatial spillover effects using traditional tests, we consider spatial network characteristics to enrich the notion of local coupling and telecoupling from a relational perspective. First, we adopt coupling coordination degree model (CCDM) and decoupling model (DM) to calculate the urbanization and eco-environment coupling coordination degree (UECCD) and the decoupling index (DI) in 30 provinces and municipalities of China from 2008 to 2017. Second, we use gravity model to construct urbanization and eco-environment coupling coordination network (UECCN), in which provinces are nodes and spatial connection relationships of UECCD are edges between nodes. Third, we introduce social network analysis (SNA) to reveal spatial network characteristics of UECCN without using local spatiotemporal heterogeneity. Finally, we employ spatial econometric model to reveal factors that influence urbanization and eco-environment coupling effect. The major findings and conclusions of this study are summarized as follows. (1) The main subclasses of UECCD and DI are basically uncoordinated patterns with eco-environment lagging and weak decoupling, respectively. (2) Only two spatial agglomeration types of UECCD exist, the high-high (H-H) clustering in Shanghai and the low-low (L-L) clustering in western China, whereas no significant spatial agglomeration effect is observed among most provinces. (3) The distribution characteristics of UECCN are sparse in western China and dense in eastern China, and the spatial correlation strength of UECCN improves. (4) Technological innovation plays a critical role in promoting UECCD, while the total population, per capita disposable income, coupling network structure, and environmental regulations exert significant impact on UECCD. Collectively, we propose to prioritize governance provinces with low UECCD in western China as well as adequately utilize the positive externalities of key node provinces in eastern China. Equally importantly, we suggest that it is also critical to fully exert a driving force of technological innovation on improving the UECCD by promoting renewable energy utilization.

Different types of industrial agglomeration and green total factor productivity in China: do institutional and policy characteristics of cities make a difference?

This paper applies panel data of 281 prefecture-level cities in China from 2004 to 2017 to calculate the green total factor productivity (GTFP) at the city level. Then, it examines the influences of various forms of industrial agglomeration on GTFP and its two decomposition factors. The results demonstrate that the agglomeration of the manufacturing industry rather than producer services can effectively improve the GTFP of cities mainly through stimulating internal technological progress. However, the collaborative agglomeration of manufacturing and producer services has a significant positive correlation with GTFP. In addition, the heterogeneity analysis shows that for cities with institutional and policy advantages, collaborative agglomeration can play a more substantial effect. But, for ordinary cities, manufacturing agglomeration is more beneficial to furthering the growth of GTFP.

Spatial correlation and influencing factors of industrial agglomeration and pollution discharges: a case study of 284 cities in China.

Exploring the spatial correlation characteristics and influencing factors of industrial agglomeration and pollution discharge, which is of great significance to reduce industrial pollution discharge and promote China's construction of an ecological civilization. Taking 284 prefecture-level cities in China in 2017 as the research object, this study used spatial autocorrelation analysis method to explore the spatial agglomeration characteristics and spatial correlation of industrial agglomeration and industrial pollution discharge, and spatial econometric analysis method was used to explore the main factors affecting industrial pollution discharge. The research results showed that the level of industrial agglomeration in China exhibited a spatial distribution characteristic of "high in the east and low in the west". The total discharge and discharge intensity of industrial pollutants showed a spatial pattern of "high in the north and low in the south" in general, and industrial agglomeration, total discharge, and discharge intensity of industrial pollution showed significant spatial autocorrelation. Moreover, industrial agglomeration had a strong local spatial correlation with the total and intensity of industrial wastewater, industrial SO2, and industrial smoke and dust, and the main agglomeration types were high agglomeration-low pollution, low agglomeration-high pollution, and low agglomeration-low pollution. In addition, industrial agglomeration had a positive impact on the total industrial wastewater discharge, and had a negative impact on the total industrial smoke and dust discharge, industrial wastewater discharge intensity, industrial SO2 discharge intensity, and industrial smoke and dust discharge intensity.

The Carbon Emission Intensity of Industrial Land in China: Spatiotemporal Characteristics and Driving Factors

CO2 emission reduction has become a consensus all around the world. This paper investigates the spatiotemporal characteristics of industrial land carbon emission intensity (ILCEI) in China by spatial autocorrelation analysis, and applies the spatial Durbin model to reveal the influence of driving factors on ILCEI. The results indicate the following: (1) national ILCEI first shows a downward and then an upward trend during the period and presents a low pattern in both Eastern and Northeastern regions and a high pattern in the Northwestern region. (2) From a global perspective, ILCEI shows significant spatial agglomeration characteristics; from a local perspective, ILCEI is dominated by H-H and L-L agglomeration types, showing that spatial heterogeneity and spatial dependence are apparent in ILCEI. (3) ILCEI is significantly negatively affected by both R & D personnel and foreign-trade dependence, while urban population density notably has positive impacts on ILCEI. This paper is a beneficial policy practice for harmonizing the contradiction between industrial land expansion and carbon discharge.

Spatiotemporal Characteristics and Influencing Factors of Tourism-Urbanization-Technology-Ecological Environment on the Yunnan-Guizhou-Sichuan Region: An Uncoordinated Coupling Perspective.

The tourism, urbanization, technology, and the ecological environment both promote and restrict each other. Coordinating the relationship between the four is of great significance to the realization of high-quality sustainable regional development. Taking the Yunnan–Guizhou–Sichuan region as an example, this paper constructs an uncoordinated coupling model for the tourism–urbanization–technology–ecological environment system. Using exploratory spatial analysis and geographic information systems, this paper reveals the temporal and spatial evolution law affecting the uncoordinated coupling relationship between tourism, urbanization, technology and the ecological environment in the Yunnan–Guizhou–Sichuan region from 2010 to 2020, before establishing a panel Tobit model that is used to explore the factors affecting the four systems. The research shows the following: (1) The level of comprehensive development for tourism, urbanization, technology, and the ecological environment in Yunnan, Guizhou, and Sichuan has increased rapidly. Of all these, the tourism industry was the most affected by COVID-19 in 2020, while the level of urbanization, technology, and ecological environment developments in the three provinces has become similar over time. (2) Uncoordinated development between cities is a prominent problem; while the uncoordinated coupling spatial agglomeration in various regions is relatively stable, the proportion of cities with no significant agglomeration form amounts to more than 70%, with mostly low–low (L–L) and high–high (H–H) agglomeration types. (3) The degree to which uncoordinated coupling exists among the four systems in the Yunnan–Guizhou–Sichuan region is affected by many factors. Only eco-environmental pressure has a significant positive correlation with the degree of uncoordinated coupling, while the tourism scale, economic urbanization, eco-environmental response, and investment in technology have a significant negative correlation. These results provide a theoretical basis and practical references for strengthening the government’s macro-control and promoting collaborative regional development.

Identification of Priority Implementation Areas and Configuration Types for Green Infrastructure Based on Ecosystem Service Demands in Metropolitan City.

During urbanization in developing countries, fragmentation of green infrastructure due to increasing populations and the expansion of construction land leads to an extremely serious imbalance between the supply and demand for urban ecosystem services. In this study, the central city of Zhengzhou, a central city in central China, was selected as the study area and the excessive demand for six ecosystem services, namely, air purification, flood regulation, heat regulation, hydrological regulation, CO2 sequestration and recreational services, was quantitatively evaluated. The entropy method was used to calculate the weights of various ecosystem services, and spatial overlay analysis was performed to obtain the comprehensive ecosystem service excessive demand. Finally, bivariate spatial autocorrelation analysis was used to explore the response of population density to comprehensive excessive demand for ESs. The results of this study indicate that: (1) The most prevalent need is for more CO2 regulation service throughout the study area. (2) Except for hydrological regulation service, the spatial distribution of the remaining highly excessive ecosystem service demands are mostly concentrated in old neighborhoods. (3) Of the six excessively demanded economic services, rainwater regulation obtained the greatest weight, reflecting the poor urban infrastructure configuration for countering the rapidly increasing threat of flooding caused by climate change in the city. (4) The comprehensive ecosystem service excessive demand results show that there are eight priority green infrastructure implementation blocks in the central city of Zhengzhou. (5) There were three agglomeration types between population density and comprehensive excessive demand for ESs: high-high type, low-high type and low-low type. The spatial distribution characteristics of population density and comprehensive ES demand are positively correlated. The results of this study could help to provide information for decision making when delineating the priority areas and types of green infrastructure implementation in developing cities.

Geographical Distribution Characteristics of Ethnic-Minority Villages in Fujian and Their Relationship with Topographic Factors

The geographical distribution characteristics of villages characterised by ethnic minorities are determined by the selection of the site when the village was initially established. The location of inherited and well-preserved minority villages must be exceptionally compatible with the natural terrain, with a logical relationship. Nonetheless, the issue of village location, which is directly related to the development of the features of the geographical distribution, has received little attention from scholars. The average nearest proximity index, Voronoi, kernel density analysis, proximity analysis, and the Geographical Detector (GeoDetector) were used to analyse the geographic distribution characteristics of villages and their correlation with terrain, as well as the difference between the influence of each terrain factor. The findings indicated the following. (1) The geographical distribution of minority villages in Fujian Province is of the agglomeration type, with a significant “mononuclear” feature, and the topography has a facilitating effect on the clustering distribution of villages. (2) The geographical distribution of minority villages in each city of Fujian Province coexisted with the agglomeration type and the dispersion type, and the role of topography in promoting the agglomeration-type distribution of villages was not affected by the distribution density of villages. (3) The site selection of Fujian-minority villages is characterised by medium altitude, moderate slope, sun exposure, and no obvious hydrophilicity. Minority villages are mainly located in areas with an elevation of 202–647 m; a slope of 6–15°; a flat land aspect with a south slope, southeast slope, or southwest slope; and distance of 500–1500 m from 5–20 m wide rivers of level 2. (4) The site selection of Fujian minority villages is influenced by various topographic factors, such as elevation, slope, aspect, river buffer, river width, and river level, among which river width has the most substantial effect. (5) All topographic factors have a two-factor enhancing relationship with each other, aspect and slope have the most substantial effect and play a dominant role in site selection. The research findings illuminate the internal logic of the geographical distribution differentiation of villages characterised by ethnic minorities, which is critical for promoting the protection of modern ethnic-minority villages.

Effect of High-Tech Manufacturing Co-Agglomeration and Producer Service Industry on Regional Innovation Efficiency

The study discusses the effect of co-agglomeration between the producer service industry and the high-tech manufacturing industry on regional innovation efficiency. Based on data from public companies of three urban agglomerations from 2011 to 2019, we used the Data Envelopment Approach (DEA)- Banker, Charnes, Cooper (BCC) model to estimate real innovation efficiency. Results found that the industrial co-agglomeration and regional innovation efficiency have an “inverted U-shaped” relationship. The industrial co-agglomeration in regions with a low level of co-agglomeration plays an important role in expediting regional innovation efficiency than that in high-level areas of co-agglomeration. Moreover, it is confirmed that the prefecture-level cities of the three urban agglomerations have low innovation efficiency types and low collaborative agglomeration types. Yangtze and Pearl river delta urban agglomeration can promote innovation efficiency through industrial co-agglomeration. While for the industrial co-agglomeration of Beijing, Tianjin, and Hebei, the urban agglomeration has not become the main way to promote innovation efficiency. The regression results of different industry collaborative agglomeration found that the co-agglomeration of information transmission, computer services, software industries, and the high-tech manufacturing industry plays a significant role to improve innovation efficiency. Moreover, the co-agglomeration of the transportation service industry and high-tech manufacturing industry plays a relatively weak role in regional innovation efficiency. Therefore, it is suggested to formulate more adaptive and heterogeneous market policies. The paper provides an important idea for improving innovation efficiency by optimizing industrial spatial layout.

Agglomeration Regimes of Particles under a Linear Laminar Flow: A Numerical Study

In this work, a combined smoothed particle hydrodynamics and discrete element method (SPH-DEM) model was proposed to model particle agglomeration in a shear flow. The fluid was modeled with the SPH method and the solid particles with DEM. The system was governed by three fundamental dimensionless groups: the Reynolds number Re (1.5~150), which measured the effect of the hydrodynamics; the adhesion number Ad (6 × 10−5~6 × 10−3), which measured the inter-particle attraction; and the solid fraction α, which measured the concentration of particles. Based on these three dimensionless groups, several agglomeration regimes were found. Within these regimes, the aggregates could have different sizes and shapes that went from long thread-like structures to compact spheroids. The effect of the particle–particle interaction model was also investigated. The results were combined into ‘agglomeration maps’ that allowed for a quick determination of the agglomerate type once α, Re, Ad were known.

Industrial agglomeration and industrial SO2 emissions in China's 285 cities: Evidence from multiple agglomeration types

The environmental externalities of industrial agglomeration have generated intense debate, yet few studies have considered their effects and inner influence mechanism based on varied agglomeration types. This study distinguished between industrial density and proximity (spatially) and industrial specialization and diversity, as well as related variety and unrelated variety (organizationally). Using the panel data from China's 285 cities from 2003 to 2013, we examined the different effects of multiple agglomeration types of industrial agglomeration on industrial SO2 emissions, and their inner influence mechanism from two aspects of industrial structure and technological progress. First, we found that various agglomeration types have different environmental externalities and that industrial density and proximity both have significant reduction effects on SO2 emissions; diversity and related variety effectively reduce SO2 discharges, but specialization and unrelated variety are linked to increased emissions. Second, technological progress and industrial structure are the critical channels of industrial agglomeration affecting pollution emissions while technological progress plays a greater role in the reducing emission effect of industrial agglomeration. Our findings reveal the importance of different agglomeration types and technological progress between industrial agglomeration and pollution emission.

The identification, logic and enlightenments of intra-urban place communities in China

Spatial agglomeration phenomena on the earth permeate in various fields of the natural and human world, yet their researches in human society are relatively few with the focus mainly on the economic concept of “industrial clusters”. Precise quantitative descriptions, in-depth logical analyses and proper application approaches for urban planning are lacked in various intra-urban spatial agglomeration phenomena. By using over 10 million POIs in the mainland China, 18 grid network models with two varieties of spatial relationships (co-location/adjacent) are constructed in this article. 23 typical place communities are extracted based on complex network analysis, and four types of agglomeration driving forces are summarized. A comprehensive demonstration displaying the application process of co-location/adjacent place matrices in auxiliary decision of the implanted place types is carried out with the example of the revitalization project of Taoxichuan Area in the city of Jingdezhen.

The Impact of the Dynamics of Agglomeration Externalities on Air Pollution: Evidence from Urban Panel Data in China

Air pollution in China has become a matter of increasing public concern. In this paper, we attempted to build a theoretical model to explore the impact of the dynamics of agglomeration externalities on haze pollution in urban China, where agglomeration is differentiated by regional specialization and geographical concentration. Based on China’s panel data for 289 cities during the period of 1998–2018, the empirical result shows that the relationship between industrial agglomeration and urban haze pollution is not simply linear or of an inversed U-type but turns out to be dynamically N-shaped. To be specific, the increase in local haze pollution can be explained by agglomeration externalities in the beginning stage, whereas the reducing effect only occurs during the mature stage. The heterogeneity test indicated that the effect of the type of agglomeration on haze pollution seems to be mixed in different groups of cities, but is still consistent with the hypothesis of the dynamic change of agglomeration externalities. The results are found to be quite robust and consistent after replacing variables and using other regression methods. This paper provides answers to the question of how to coordinate the relationship between developing industry parks and air pollution in terms of the life cycle of agglomeration as well as the types of city.