Agglomeration Patterns Research Articles

Evaluation and optimization of ecological landscape pattern for urban agglomeration in the Pearl River Delta, China.

The evaluation and optimization of landscape ecological pattern has important implications for the accurate improvement of forest quality and high-quality urban development in the Pearl River Delta urban agglomeration. Based on the "one map" data and digital elevation model data of forest resource management in 2021, we evaluated and optimized landscape ecological pattern of the Pearl River Delta urban agglomeration by morphological spatial pattern analysis and minimum cumulative resistance model. The results showed that there were 435861 patches in the Pearl River Delta urban agglomeration that could be used as ecological source area, covering an area of 7346.60 km2 and accounting for 13.4% of the Pearl River Delta area. Thirty patches were selected as the ecological source area of the study area by using the area and patch importance index, covering an area of 2792.59 km2 and accounting for 5.1% of the Pearl River Delta area. The overall natural environment of the Pearl River Delta urban agglomeration was excellent. The ecological resistance level was small. The peripheral ecological resistance was low. The core ecological resistance was high. There was still a large room for adjustment of stand types and landscape patterns, which should be optimized by adjusting the composition and spatial distribution of tree species. The ecological network of the Pearl River Delta urban agglomeration was optimized with 30 ecological sources, 103 key ecological corridors, and 95 ecological nodes. The improvement rates of the optimized probability of connectivity index and integral index of connectivity index were 297.5% and 695.1%, respectively. The optimization results could effectively connect the ecological sources and spread the ecological service functions of ecological sources.

Urbanization and the Emerging Water Crisis: Identifying Water Scarcity and Environmental Risk with Multiple Applications in Urban Agglomerations in Western China

Urbanization and climate change have combined to exacerbate water shortages in cities worldwide. While rapid urbanization is faced with the risk of water resource shortage, there are few studies on the impact of water resource shortage and the ecological environment in mega-regions. Taking the three major urban agglomerations in Western China as an example, the spatial–temporal agglomeration pattern and driving force for the risk of water shortage are analyzed. First, a new comprehensive index system for environmental risks of water resources has been established, which can be used to assess spatial changes in water resource shortage risks. Secondly, the relationship between water resource shortage and the urban agglomeration effect is discussed in regards to water resource vulnerability, exposure, and recoverability. The results showed: (1) From 2000 to 2018, the risk of total water shortage in 12 provinces (cities) in Western China decreased from 3.42 to 2.59; the risk of total water shortage in the Guanzhong Plain urban agglomeration dropped the fastest, with an average annual decline rate of 10.57%. (2) Water resource shortage in different cities of the three major urban agglomerations is out of sync in time and space; the risk level of water shortage is high in the north and low in the south. (3) Geological environmental change is an important influencing factor of water resource shortage; the negative impact of industrial water use on the risk of water shortage is the largest, with a contribution of 24.9%. In addition, this paper also puts forward policy suggestions to alleviate the risks of water shortage in the urbanization process in the western region. This research can provide a scientific basis for the sustainable development of urban water resources.

Poverty, population growth and agglomeration effects in all Brazil cities

PurposeThis paper aims to test three hypotheses in city growth literature documenting the poverty reduction observed in Brazil and exploring a rich spatial dataset for 5,564 Brazilian cities observed between 1991 and 2010. The large sample and the author's improved econometric methods allows one to better understand and measure how important income growth is for poverty reduction, the patterns of agglomeration and population growth in all Brazilian cities.Design/methodology/approachThe author identifies literature gaps and use a sizeable spatial dataset for 5,564 Brazilian cities observed in 1991, 2000 and 2010 applying instrumental variables methods. The bias-corrected accelerated bootstrap percentile interval supports the author's point estimates.FindingsThis manuscript finds that Brazilian data for cities does not support Gibrat's law, raising the scope for urban planning and associated policies. Second, economic growth on a sustainable basis is still a vital source of poverty reduction (The author estimates the poverty elasticity at four percentage points). Lastly, agglomeration effects positively affect the city's productivity, while negative externalities underlie the city's development patterns.Originality/valueData for cities in Brazil possess unique characteristics such as spatial autocorrelation and endogeneity. Applying proper methods to find more reliable answers to the above three questions is a desirable procedure that must be encouraged. As the author points out in the manuscript, dealing with endogenous regressors in regional economics is still a developing matter that regional scientists could more generally apply to many regional issues.

Spatiotemporal evolution and driving factors of urban green technology innovation efficiency in the Chengdu-Chongqing Economic Circle of China

IntroductionGreen technology innovation efficiency (GTIE) growth is an essential route to protect the urban ecological environment in the Chengdu-Chongqing Economic Circle (CCEC). However, the measurement and spatial driving factors of GTIE are still puzzled. MethodsThis study constructs an urban GTIE indicator system including inputs, desired outputs, and undesired outputs, and evaluates urban GTIE in the CCEC using the super-efficiency slacks-based measure (S-SBM). Then, the exploratory spatial data analysis (ESDA) method is applied to analyze the geographical distribution and spatial correlation characteristics of urban GTIE, and a spatial econometric model is used to analyze the influencing factors of urban GTIE from the perspective of spatial spillover. ResultsThe results suggest that: (1) From 2006 to 2020, the urban GTIE of CCEC has obviously increased, and its spatial distribution has a prominent unbalanced feature. (2) The urban GTIE of CCEC mainly presents a significant spatial positive correlation, mainly manifested in the "high-efficiency type" and "low-efficiency type" regional agglomeration patterns, and the spatial "core-edge" structure centering on Chengdu and Chongqing tends to be stable. (3) Economic development (ED), government support (GS), and environmental regulation (ER) can promote the urban GTIE. The negative spillover effects of external opening (EO) and ER are significant, and they have negative effects on the GTIE of neighboring cities. (4) Spatial heterogeneity analysis shows that the driving factors of urban GTIE with different efficiency levels are significantly different, and the spatial spillover effect of the driving factors of GTIE is more significant in high-efficiency cities.DiscussionThe urban GTIE in the CCEC is showing a growth trend, but it needs to narrow the gap in GTIE between cities. Firstly, cities can improve the GTIE by improving ED, strengthening GS, and enhancing ER. Secondly, cities need to pay attention to the negative spatial spillover effects of ER and EO in the process of improving the GTIE. Finally, cities with different efficiency types need to develop differentiated green innovation development strategies based on their own conditions.

Spatial correlation and coupling between industrial enterprise agglomeration and water pollutant discharge

Industrial wastewater discharge has become the main cause of water pollution in China. However, the spatial interaction mechanism between industrial structure and water pollution is still unclear. Accordingly, we evaluated and analyzed spatiotemporal changes of the agglomeration pattern of pollution-intensive industrial enterprises and the evolution of the water environmental pollution pattern, as well as the correlation between them. The study results show that the polluting industrial enterprises were located mainly along the Yangtze River and Taihu Lake Basin in southern Jiangsu in 2013 and 2018. However, we observed a spatial trend of pollution transfer to northern Jiangsu. The industrial water pollution discharge presents the distribution pattern of facing rivers, seas, lakes and cities. Papermaking and paper products industry are the leading factors of COD and NH3–N pollution, with explanatory power of 0.3666 and 0.6201 respectively. The spatial positive coupling effect between the concentration degree of polluting enterprises and the intensity of water environment pollution discharge is 94.95% of the region. The spatial agglomeration of polluting industrial enterprises is an important cause of water environment pollution. They promote and couple each other, proving the existence of “Pollution haven” and “Porter hypothesis".

The Spatiotemporal Evolution and Influencing Factors of the Ceramics Industry in Jingdezhen in the Last 40 Years

The ceramic industry has been vital to the city’s development and prosperity in Jingdezhen, but the development of the ceramics industry in Jingdezhen has been unclear since China’s economic reforms, which will become a bottleneck limiting the sustainable development of the city. This study explored the spatial agglomeration and spatiotemporal evolution of the ceramics industry in Jingdezhen from 1980 to 2020 using enterprise directory data. The study opted for a microscopic perspective and employed kernel density estimation and exploratory spatial data analysis to obtain the necessary results. It also analyzed the influencing factors using a Geodetector. The results show that the temporal evolution of the ceramics industry in Jingdezhen went through two stages from 1980 to 2020. The number of enterprises experienced exponential growth, with fluctuations. The spatial evolution of the ceramics industry transitioned from a “single-center” to a “double-center” model and further evolved into a “multi-center” model. Moreover, the spatial agglomeration of the ceramics industry underwent the process of “agglomeration-diffusion-polarization”, ultimately developing into four ceramic industrial agglomeration patterns in six hotspots. Agglomeration, historical, technological, policy, and transportation factors had positive effects on the evolution of the ceramics industry in Jingdezhen, with agglomeration being the top contributor. Likewise, there were obvious interactions between the factors. This study can provide a basis for formulating policies to support urban spatial planning for urban revitalization, and provide foundation for the development of the national ceramic culture inheritance and innovation pilot zone in Jingdezhen.

Spatiotemporal Evolution and Correlation Analysis of Carbon Emissions in the Nine Provinces along the Yellow River since the 21st Century Using Nighttime Light Data

Monitoring carbon emissions is crucial for assessing and addressing economic development and climate change, particularly in regions like the nine provinces along the Yellow River in China, which experiences significant urbanization and development. However, to the best of our knowledge, existing studies mainly focus on national and provincial scales, with fewer studies on municipal and county scales. To address this issue, we established a carbon emission assessment model based on the “NPP-VIIRS-like” nighttime light data, aiming to analyze the spatiotemporal variation of carbon emissions in three different levels of nine provinces along the Yellow River since the 21st century. Further, the spatial correlation of carbon emissions at the county level was explored using the Moran’s I spatial analysis method. Results show that, from 2000 to 2021, carbon emissions in this region continued to rise, but the growth rate declined, showing an overall convergence trend. Per capita carbon emission intensity showed an overall upward trend, while carbon emission intensity per unit of GDP showed an overall downward trend. Its spatial distribution generally showed high carbon emissions in the eastern region and low carbon emissions in the western region. The carbon emissions of each city mainly showed a trend of “several”; that is, the urban area around the Yellow River has higher carbon emissions. Meanwhile, there is a trend of higher carbon emissions in provincial capitals. Moran’s I showed a trend of decreasing first and then increasing and gradually tended to a stable state in the later stage, and the pattern of spatial agglomeration was relatively fixed. “High–High” and “Low–Low” were the main types of local spatial autocorrelation, and the number of counties with “High–High” agglomeration increased significantly, while the number of counties with “Low–Low” agglomeration gradually decreased. The findings of this study provide valuable insights into the carbon emission trends of the study area, as well as the references that help to achieve carbon peaking and carbon neutrality goals proposed by China.

Research on the Level of Agricultural Green Development, Regional Disparities, and Dynamic Distribution Evolution in China from the Perspective of Sustainable Development

Green development is a concept of sustainable development, aiming to protect the environment and ecosystems while meeting economic development needs. In the field of agriculture, green development has emerged as a crucial pathway for reconciling the conflicts between agricultural development and ecological conservation. To investigate the level of green development in Chinese agriculture, regional variations, and the evolutionary patterns, this paper is based on the framework of sustainable development theory. This study establishes a comprehensive evaluation system for agricultural green development and applies methods such as entropy-weighted TOPSIS, Dagum’s Gini coefficient, kernel density estimation, Moran’s I index, and Markov chains to analyze the level of agricultural green development, regional disparities, and dynamic evolution in China. The findings of this study reveal that: (1) The overall level of agricultural green development in China is steadily improving, with notable differences in the level of agricultural green development among different regions and provinces. There are significant disparities in agricultural green development between regions, and the overall disparities exhibit a fluctuating downward trend characterized by periods of increase followed by decrease. The regional disparities are identified as the primary cause of the overall disparities in agricultural green development in China. (2) The eight major economic regions in China are experiencing steady development in agricultural green practices, but there are varying degrees of polarization due to different development speeds. (3) This study also highlights a clear spatial positive correlation in the level of agricultural green development in China, with most provinces showing clustering in the first and third quadrants, indicating a “high–high” (H-H) and “low–low” (L-L) agglomeration pattern. (4) The study reveals that the level of agricultural green development in China exhibits a certain degree of stability. Over time, the probability of transitioning from lower-level regions to neighboring higher-level regions increases, and the agricultural green development level in neighboring regions can influence the spatial transfer probability within a given region. Therefore, agricultural green development demonstrates significant spatial dependence.

Agglomeration effect on biomechanical performance of CNT-reinforced dental implant using micromechanics-based approach

Dental implants have long played an important role in restoring lost teeth, but there are still concerns about their durability and long-term success. Commercial dental implants have traditionally been made of metallic and ceramic materials like titanium and zirconia; however, each kind of material has restrictions regarding osseointegration and mechanical characteristics that differ between native bone and the implant material, limiting the implant's longevity and reliability. To address these concerns, this research explores the use of carbon nanotubes (CNTs) in restorative dentistry, their excellent properties make them an ideal candidate for promoting bone growth around implanted device and ensuring long-lasting success. The objective of this study was to understand how CNT properties when incorporated into the titanium matrix may be able to better adapt to the oral environment taking into consideration the CNT agglomeration effects when designing reinforced nanocomposite materials for dental implant. A mathematical formulation of the micromechanics model was developed and improved to extend its application for the case of CNT-based composite materials for dental implants. A three dimensional (3D) model of bone structure around the osseointegrated dental implant was established considering different compositions of implant material. Finite Element Analysis (FEA) were conducted to assess the aggregation effect of implant incorporating CNTs into the titanium matrix, considering CNTs with both spherical inclusions (CNT clusters), and randomly dispersive ones (CNTs) in the titanium matrix, on osseointegration and bone remodeling around the dental implant and supporting bone system over a period of 48 months. Firstly, the effects of CNT-Ti implantation on time-dependent performance are evaluated in a computational remodeling framework. Then, Von Mises equivalent stresses are investigated to evaluate the stress distributions and micromotions in jaw bones of loaded implant with different composition of prosthetic material. Three agglomeration patterns are considered, particularly without agglomeration (ζ = ξ), partial and complete agglomeration (ζ < ξ, ξ = 1). Further, the influence of CNTs volume fraction variation is taken into account to predict the mechanical response of the bony system after CNT-reinforced dental implantation. It can be inferred that the agglomeration of CNTs reduces the elastic stiffness of the matrix. This is due to the fact that when CNTs are agglomerated, the inter-tube contacts are reduced and the effective stiffness of the matrix is decreased.

Spatiotemporal Evolution of Cultivated Land Use Eco-Efficiency and Its Dynamic Relationship with Landscape Pattern Change from the Perspective of Carbon Effect: A Case Study of Henan, China

Cultivated land is a vital factor in agricultural production but faces multiple challenges, including declining total area, spatial transformation, and ecological degradation. It is imperative to enhance cultivated land use eco-efficiency (CLUE). This study aimed to evaluate the CLUE that considers both carbon sequestration and emissions using the SBM model at the county level. Next, spatial autocorrelation was employed to measure CLUE’s spatial correlation. The spatial agglomeration pattern of CLUE was determined, then time-series cluster analysis was used to identify the temporal evolution patterns of CLUE in various districts and counties. Furthermore, we explored the spatiotemporal dynamic relationship between CLUE and landscape pattern changes using landscape pattern index and geographically and temporally weighted regression (GTWR), considering spatiotemporal heterogeneity, and using interaction detectors to identify the interaction between landscape pattern factors on CLUE. The results show that: (1) From 2000 to 2020, CLUE in Henan Province varied between 0.50 and 0.70 in most years, indicating potential for improvement. There are four primary temporal evolution patterns: 26 Late-development, 22 Wave-rising, 27 Fluctuation-rising, and 29 Continuous-rising types of CLUE. (2) CLUE exhibits low values in the middle and eastern regions, i.e., areas with high values are concentrated in the southern districts, counties, and western and northern regions. CLUE has a significant positive spatial correlation with HH agglomeration areas mainly concentrated in Xinyang City, and LL agglomeration areas mainly located in the eastern and central regions. (3) Overall, different landscape factors exhibit varying degrees of spatiotemporal heterogeneity in their impact on CLUE. The total area and aggregation of cultivated land have a positive effect on CLUE, with the area of the positive influence of the total area gradually expanding over time and the aggregation gradually decreasing. The complexity of cultivated land shape has a negative effect. The impact of cultivated land patch density is two-sided, with the area of negative influence gradually expanding over time. (4) The interaction between the total area, shape, and aggregation of cultivated land is enhanced. Additionally, the interaction between cultivated land patch density and other factors has changed from a weakening to a strengthening one, and the “double-edged sword” effect has gradually shifted into a one-way effect. Therefore, in the process of land consolidation, it is recommended to prioritize regularized, larger, and more concentrated cropland patches whenever possible.

Impact of Financial Agglomeration on Regional Economic Growth in China: A Spatial Correlation Perspective

In the contemporary world, finance is crucial to economy and financial agglomeration has a lasting effect on the economic growth. The areas of financial agglomeration will influence the economy of the local parts throughout agglomeration effect, and have an impact on its spatially related parts. Based on Chinese inter-provincial panel data from 2007 to 2018, this study constructs an indicator system of financial agglomeration and measures provincial agglomeration levels to examine its impact on regional economic growth. The results show that, first, the level of financial agglomeration has a trend of steady change and presents a ladder-decreasing agglomeration pattern. Second, financial agglomeration could effectively promote the economic growth of local and spatially related regions. The development of the financial industry depended more on geographical distance, and the diffusion effect of financial agglomeration in the eastern, central, and western regions are in different stages. Finally, by summarizing the research conclusions, this study put forward some suggestions for China’s regional economic growth.

Global Competitiveness of Trade in the West Coast of Sumatra from the Perspective of the Agglomeration of Economic Approach

The main aim of this study is to analysed the pattern of agglomeration of trade flows in the city of Padang and Teluk Bayur Port with the regencies and cities along the west coast of Sumatra, as well as the factors that influence them. How competitive is trade flow with the West coast region compared to the East coast of Sumatra. The analytical approach used is spatial econometrics, especially the spatial lag model and spatial error model. The data used is the volume of loading and unloading of inter-island and foreign ships at Teluk Bayur Port and other districts and cities in 133 areas on the West Coast and 155 districts and cities in the economic corridors of Sumatra. The results showed that the West Coast trade flow variable had no significant effect on the existence of Padang City as the centre of economic agglomeration in the economic corridor area of Sumatra. Our findings are that trade flows in the city of Padang have not been able to encourage economic agglomeration in regencies and cities on the West Coast of Sumatra, on the contrary there are connections with regencies and cities in the economic corridors of the East Coast of Sumatra. Therefore, to accelerate the process of economic agglomeration through trade flows, joint policies are needed with the agricultural sector in the Sumatran economic corridor in facing global market competition, as well as strengthening inter-regional internal markets in the Sumatra corridor, agricultural commodity transactions between regencies and cities in the Sumatran economic corridor which must be strengthened, so as to be able to compete in a competitive global market.

Global Competitiveness of Trade in the West Coast of Sumatra from the Perspective of the Agglomeration of Economic Approach

The main aim of this study is to analysed the pattern of agglomeration of trade flows in the city of Padang and Teluk Bayur Port with the regencies and cities along the west coast of Sumatra, as well as the factors that influence them. How competitive is trade flow with the West coast region compared to the East coast of Sumatra. The analytical approach used is spatial econometrics, especially the spatial lag model and spatial error model. The data used is the volume of loading and unloading of inter-island and foreign ships at Teluk Bayur Port and other districts and cities in 133 areas on the West Coast and 155 districts and cities in the economic corridors of Sumatra. The results showed that the West Coast trade flow variable had no significant effect on the existence of Padang City as the centre of economic agglomeration in the economic corridor area of Sumatra. Our findings are that trade flows in the city of Padang have not been able to encourage economic agglomeration in regencies and cities on the West Coast of Sumatra, on the contrary there are connections with regencies and cities in the economic corridors of the East Coast of Sumatra. Therefore, to accelerate the process of economic agglomeration through trade flows, joint policies are needed with the agricultural sector in the Sumatran economic corridor in facing global market competition, as well as strengthening inter-regional internal markets in the Sumatra corridor, agricultural commodity transactions between regencies and cities in the Sumatran economic corridor which must be strengthened, so as to be able to compete in a competitive global market.

Multi-Scale Analysis of PM2.5 Concentrations in the Yangtze River Economic Belt: Investigating the Combined Impact of Natural and Human Factors

Air pollutants, primarily PM2.5, have inflicted significant harm on public health and sustainable urban development in the Yangtze River Economic Belt (YREB). Previous studies often neglected the coordinated measurement of PM2.5 human and natural factors in this area. Therefore, this paper focuses on the YREB. Using a geographic information system (GIS) platform, along with remote sensing and statistical data spanning from 2000 to 2020, this study employs spatial analysis to uncover the spatial-temporal characteristics of PM2.5 and its spatial agglomeration patterns. Furthermore, this study further employs the spatial panel Durbin model to investigate the natural and anthropogenic factors driving PM2.5 concentrations across multiple scales. The analysis of the results reveals an “M”-shaped change trend in PM2.5 concentrations within the YREB. PM2.5 concentrations exhibit significant spatial agglomeration characteristics, whereby most urban agglomerations are high-pollution areas. Moreover, the changes in PM2.5 concentrations are jointly influenced by several factors, including the secondary industry, urban built-up area, population density, annual precipitation, and NDVI. Furthermore, the dominant factors influencing PM2.5 concentrations in the three major urban agglomerations exhibit both similarities and differences. In addition, for effective governance coordination across regions, policymakers should diligently consider both the shared predominant factors and the varying factors specific to each region in the future. This study expands the research content of watershed PM2.5 collaborative governance, and further provides practical support for other watershed environmental governance and urban sustainable management.

Evaluation of Biodiversity Maintenance Capacity in Forest Landscapes: A Case Study in Beijing, China

Understanding how landscape pattern changes impact forest biodiversity conservation and ecosystem management is crucial. This study evaluated the biodiversity maintenance capacity (BMC) of forest landscapes in Beijing, China from 2005 to 2020 based on habitat quality and carbon sink. For this, the moving window method was employed to compute landscape indices that depict variations in landscape patterns, including intermixing, connectivity, diversity, and compactness. Lastly, the relationship between landscape pattern changes and the BMC of forest landscapes was investigated using a combination of spatial correlation analysis and geographic weighted regression measurement models. The results showed the following. (1) The average BMC increased from 0.798 to 0.822. Spatially, 84.14% of the areas experienced an improvement, mainly in the mountainous region. In contrast, a decrease was observed in 4.03% of the areas, primarily concentrated in the transition zone between mountains and suburban plain. (2) The landscape pattern changed dramatically from 2005 to 2020. Landscape intermixing and compactness decreased slightly by 11.45% and 7.82%, while landscape connectivity and diversity increased significantly by 64.28% and 55.44%, respectively. (3) The BMC’s global Moran’s I values in 2005 and 2020 were 0.711 and 0.782, respectively, signifying a spatial bipolar agglomeration pattern. (4) Among the four selected landscape indices, the compactness was found to be the most critical factor. It attained a positive contribution to forests with high BMC, but had a negative impact on forests with low BMC. The results could provide a reference for planners coordinating forest management and biodiversity conservation.

Do creative industries enhance employment growth? Regional evidence from Colombia

ABSTRACT Do creative industries have positive spillovers for the local economy in middle-income countries? While in high-income countries several studies have shown that creative industries are highly innovative and productive, positively impacting the local economy, the evidence is scarce for middle-income countries. Using employment data, we studied the agglomeration patterns of creative industries in Colombia between 2008 and 2017. We found a positive relationship between creative industries’ agglomeration and employment in non-creative services industries. However, using a shift–share instrumental variable approach, we found no significant causality of an increase in creative industries’ employment on employment growth in other industries.

Spatial-Temporal Characteristics and Influencing Factors of Carbon Emissions from Land Use and Land Cover in Black Soil Region of Northeast China Based on LMDI Simulation

Land use change accounts for a large proportion of the carbon emissions produced each year, especially in highly developed traditional heavy industry and agriculture areas. In this study, we estimated the carbon emissions from land use in the Black Soil Region of Northeast China (BSRNC) from 1990 to 2020. We utilized seven periods of land use remote sensing image data spanning the years 1990, 1995, 2000, 2005, 2010, 2015, and 2020, with a 30-m grid resolution. Additionally, socio-economic data was incorporated into the analysis. The preprocessing of the remote sensing images involved several steps using ENVI 5.5, including radiometric correction, fusion, mosaic, and cropping. The land types were classified into six major categories: cropland, forest land, grassland, water area, construction land, and unused land, using the LUCC classification system. The IPCC coefficient method was used to calculate the trends in carbon emissions from land use, and the logarithmic mean Divisia index (LMDI) method was applied to analyze the influencing factors. The main conclusions are as follows: (1) From 1990 to 2020, the net carbon emissions from land use in the BSRNC increased from 11.91 × 104 t to 253.29 × 104 t, with an annual growth rate of 8.04%. (2) Spatially, land use carbon emissions exhibited an agglomeration pattern that gradually weakened and the regional emission differences gradually narrowed. (3) Income level was identified as the most important factor influencing land use carbon emissions in the BSRNC from 1990 to 2020. Land use efficiency had a inhibitory effect on net carbon emissions, reducing land use carbon emissions by 1730.63 × 104 t.

Do high local customer and supply densities foster firm growth?

We analyze the effect of local supplier and customer densities on employment growth, using a sample of 19,275 German firms. We suggest a new approach to estimating firm-specific customer and supply densities through combining input-output data from the German Federal Statistical Office and firm-level data from the Orbis database. Existing empirical analyses are primarily conducted on a regional level, so that findings may depend on the regarded regional level and only provide limited insights into the existence and effects of agglomeration on growth at the firm-level. We use kernel density estimation to avoid the arbitrariness of spatial boundaries and scales, and find that the regional agglomeration patterns vary considerably between different sectors. Our econometric analysis reveals that input supplier and customer densities have a statistically significant effect on firm growth and that the firms’ age plays a crucial role in analyzing agglomeration economies.

Estimate of population density and diagnosis of main factors of spatial heterogeneity in the metropolitan scale, western China

We estimated the population density and quantified its characteristics using remote sensing, census data, and Geographic Information System (GIS). The interactive influence of these factors on population density was quantified based on geographic detectors to identify the differentiation mechanisms in the Chengdu metropolitan area of China. We identified the key factors that contribute to population density growth. The models used to simulate population density had the highest R2 values (>0.899). Population density tended to increase with time, with a multicentre spatial agglomeration pattern; the centre of gravity of the spatial distribution tended to move from the southeast to the northwest. Industry proportions, Normalised Difference Vegetation Index (NDVI), land use, distance to urban centers or construction land, and GDP per capita can satisfactorily explain population density changes. The combined impact of these elements on population density variation exhibited mutual and non-linear strengthening, with the mutual effect of the two elements intensifying the impact of each individual element. Our study identified the key driving forces that contribute to the differentiation of population density, which can provide valuable support for the development of effective regional and targeted population planning guidelines.

Sustainable network analysis and coordinated development simulation of urban agglomerations from multiple perspectives

Sustainable and coordinated development plays a crucial role in urban planning. However, previous studies have not fully explored the complex interplay between different factors that affect coordinated development within urban agglomerations. To address this gap, our study proposes a novel network-based approach that combines qualitative analysis and quantitative verification to assess the coordinated development of urban agglomerations. In this study, we focused on the Yangtze River Delta Urban Agglomeration (YRDUA) as our study area. Firstly, we constructed a network for sustainable development in urban agglomerations based on Sustainable Development Goals (SDG) data. Next, we analyzed the spatial patterns of intercity resource exchange flows to gain a more comprehensive and diversified perspective on regional coordinated development. By integrating qualitative and quantitative analysis, we then used the Exponential Random Graph Model (ERGM) to identify the driving factors of coordinated development. Finally, we optimized the development score of urban subgroups by using a greedy algorithm and determined the direction for coordinated development among different cities in the YRDUA. The findings of our study reveal that the development potential and optimal agglomeration pattern of urban subgroups in the YRDUA are closely linked to intercity cooperation, science and technology industries, foreign investment, and multiple interaction flows. Furthermore, our results suggest that local governments should focus on building a multi-core network flow pattern and fully utilizing the distinctive advantages of urban subgroups according to local conditions and time. These insights can guide policymakers and urban planners in designing more coordinated and efficient urban agglomerations.