Search Results (28)

In this paper, we present the concept of “sequencing economics”, consisting of (A) segmentation, (B) construction sequencing, and (C) functions. An agglomeration is organized into segments, and sequencing economics examines the sequential process of efficiently building such segments. The functions (C) of the segments act as a master switch, an accelerator, a brake, etc. in the implementation of agglomeration policy. In this paper, we identify a master switch and an accelerator in scientific city agglomeration policy and draw two conclusions. First, in agglomeration policy, the construction of the master switch lowers “transport costs”, as derived from the monocentric city model of spatial economics by Fujita and Krugman. Second, the accelerator segment represents the activities of the service sector that have the highest forward-linkage effect in an input–output relationship. Regarding science city agglomeration policy, it can be concluded that the master switch is high-speed rail and the accelerator is research and education activities. In this paper, the new scientific urban agglomeration that emerges from monocentric cities is referred to as railroad-driven agglomeration (RDA), which is a type of transit-oriented development (TOD). This paper demonstrates that the Tsukuba Express, as a case study of RDA, caused the agglomeration of Tsukuba Science City. This paper establishes the concept of sequencing economics, a policy implementation rule that differs from Tinbergen’s rule. The latter is based on the concept of simultaneous equations, whereas the rule of sequencing economics is based on sequential equations. RDA enables middle-income countries to surpass their middle-income status. Full article

The digital economy has profoundly reshaped industrial organizational structures and the spatial distribution of cooperative agglomerations in the manufacturing and productive service sectors. To support the coordinated and sustainable development of China’s industries, it is essential to clarify how the digital economy influences industrial cooperative agglomeration. This study first constructs a comprehensive index system capturing the quality, quantity, and synergy of industrial cooperative agglomeration, enabling an evaluation of collaborative agglomeration levels across 30 Chinese provinces. Second, the relationship between the digital economy and industrial collaborative agglomeration is examined using both static and dynamic spatial panel models. Finally, the paper investigates regional disparities in this relationship across eastern, central, and western China. The results reveal the following findings: (1) The digital economy has a significant inhibitory effect on industrial collaborative agglomeration overall. (2) Dynamic spatial lag model results show an inverted U-shaped relationship, where the digital economy initially promotes but later inhibits industrial agglomeration, with notable temporal lags and spatial spillover effects. (3) In eastern China, digital economy growth suppresses local agglomeration while promoting it in neighboring regions; in the central region, it enhances local agglomeration but dampens it in adjacent areas; and in the western region, the relationship is nonlinear and U-shaped. Full article

The digital creative industry (DCI) has become an integral part of China’s strategic emerging industries. This paper, utilizing county-level data from Chinese digital creative enterprises in 2022, examines the locational factors influencing the spatial distribution of China’s DCI through Principal Component Analysis and Multiple Linear Regression Analysis. The findings indicate that technological innovation and the level of economic development universally and dominantly influence the agglomeration of all DCI sub-sectors. Service-oriented digital creative enterprises are more likely to cluster in areas with abundant cultural resources and public facilities, with government policies and financial subsidies playing a significant role. In contrast, digital creative equipment manufacturing companies are more likely to locate in proximity to market demand and related industries. Full article

In the new paradigm where the digital economy is profoundly reshaping urban spatial organization, how the platform economy transcends traditional geographical constraints to restructure the urban system has become a strategic issue in urban geography and regional economics. This study develops an innovative measurement framework based on Business-to-Business (B2B) e-commerce enterprises to analyze platform-driven urban systems across 337 Chinese cities. Using spatial autocorrelation, rank-size distributions, and urban scaling laws, we reveal spatial differentiation patterns of cities’ B2B platforms. Combining Ordinary Least Squares (OLS) and random forest models with Partial Dependence Plots (PDP), Individual Conditional Expectations (ICE), and Locally Weighted Scatterplot Smoothing (LOWESS), we uncover non-linear mechanisms between platform development and urban attributes. Results indicate that (1) B2B platforms exhibit “superliner agglomeration” and “gradient locking”, reinforcing advantages in top-tier cities; (2) platform effects are non-linear, with Gross Domestic Product (GDP), Information Technology (IT) employment, and service sector shares showing threshold-enhanced marginal effects, while manufacturing bases display saturation effects; and (3) regional divergence exists, with eastern consumer-oriented platforms forming digital synergies, while western manufacturing platforms face path dependence. The findings highlight that platform economy evolution is shaped by a “threshold–adaptation–differentiation” mechanism rather than neutral diffusion. This study provides new insights into urban system restructuring under digital transformation. Full article

This study investigates the impact of industrial agglomeration on the upgrading of China’s automobile industry (UCAI) using panel data from 28 Chinese provinces spanning 2000 to 2020. The automobile industry is vital to China’s manufacturing and service sectors, with its upgrading capable of driving national economic growth and contributing to sustainable development goals. We employ the Malmquist productivity index based on the Data Envelopment Analysis (DEA) method, implemented through DEAP 2.1 software, to assess the UCAI. System Generalized Method of Moments (GMM) analysis, conducted using Stata 17 software, was used to examine the impact of industrial agglomeration on this process, while also exploring the threshold effect of human capital and the moderating effect of government. The results indicate that industrial agglomeration significantly enhances the upgrading of the automobile industry; however, human capital acts as a critical threshold. Below this threshold, agglomeration does not have a significant impact on the upgrading of the automobile industry, while exceeding it allows for significant positive effects. Additionally, government has a moderating effect in facilitating this process by implementing policies that support innovation and sustainable practices. Based on these findings, this paper presents several policy implications aimed at further promoting the UCAI and advancing sustainable development in the sector. Full article

Industrial linkages play a crucial role in sustaining industrial agglomerations, driving economic growth, and shaping the spatial architecture of economic systems. This study explores the complexity of causal networks within the industrial ecosystems of China and the United States, using high-frequency economic data to compare the interdependencies and causal structures across key sectors. By employing the partial cross mapping (PCM) technique, we capture the dynamic interactions and intricate linkages among industries, providing a detailed analysis of inter-industry causality. Utilizing data from 32 Chinese industries and 11 United States industries spanning 2015 to 2023, our findings reveal that the United States, as a global leader in technology and finance, exhibits a diversified and service-oriented industrial structure, where financial and technology sectors are pivotal to economic propagation. In contrast, China’s industrial network shows higher centrality in heavy industries and manufacturing sectors, underscoring its focus on industrial output and export-led growth. A comparative analysis of the network topology and resilience highlights that China’s industrial structure enhances network stability and interconnectivity, fostering robust inter-industry linkages, whereas the limited nodal points in the United States network constrain its resilience. These insights into causal network complexity offer a comprehensive perspective on the structural dynamics and resilience of the economic systems in both countries. Full article

The accelerated growth of the global economy has given rise to a multitude of environmental concerns that demand immediate attention. At this juncture, the total global carbon emissions are exhibiting a gradual increase. China, the United States, India, Russia, and Japan represent the top five countries in terms of global carbon emissions, collectively accounting for approximately 60% of the global total. Of these, China’s carbon emissions are the highest in the world, representing over 30% of the global total. As urbanization accelerates, the carbon emissions from urban agglomerations constitute a substantial share of the nation’s total emissions, rendering the carbon emissions of urban clusters a critical issue. In the context of China’s urban agglomerations, the Beijing–Tianjin–Hebei region, due to factors such as industrial structure, accounts for a relatively high proportion of carbon emissions, approximately 11% of the national total. The future trajectory of carbon emissions in the Beijing–Tianjin–Hebei region will significantly impact the high-quality development of the entire urban cluster. Consequently, research on carbon emissions in the Beijing–Tianjin–Hebei region is of vital importance. This paper takes the carbon emissions of the power industry in the Beijing–Tianjin–Hebei region as the research subject, analyzes its carbon emissions status, and builds a multi-regional input–output model for the Beijing–Tianjin–Hebei region based on the input–output tables and carbon emissions data of each province. This study explores the key influencing factors of carbon emissions from the power industry in this region from 2012 to 2017 and analyzes the carbon emissions transfer and structural evolution from the perspective of the region and the industry to clarify the carbon reduction responsibilities of the Beijing–Tianjin–Hebei region and provide references and recommendations for the formulation of regional collaborative emission reduction policies. The results show that the direct carbon emissions from the power industry in the Beijing–Tianjin–Hebei region account for a higher proportion compared to the indirect carbon emissions it generates by driving other industries. Industries with relatively high indirect carbon emissions in the key path include coal mining and selection, equipment manufacturing, transportation, services, etc. The capital input process from Tianjin and Hebei to Beijing is accompanied by a relatively high carbon transfer. Promoting the widespread adoption of carbon emission reduction technologies will have an effective suppressive effect on carbon emissions in the Beijing–Tianjin–Hebei region, especially in Hebei; Beijing and Tianjin should pay attention to the stimulating effect of increased final demand on carbon emissions; the transfer of carbon emissions between regions and industries shows a downward trend as the power sector undergoes transformation. Full article

Jiangsu province’s forestry industry offers valuable insights for resource-poor regions in China. This study aims to determine the competitiveness of different forestry sectors in Jiangsu province and identify the industrial structural changes in this context. The shift-share method is employed to analyze the primary, secondary, and tertiary forestry industries, encompassing 17 sub-sectors, and investigate the industrial regional types. By combining location quotient values and inter-sector characteristics, the leading sectors are identified. The findings reveal that the Kuznets facts and the Engel effect in the industry structure have facilitated the process of increasing the value of ecological products; this is particularly evident between 2002 and 2018, as well as in 2021. Within the primary forestry industry, the Flower cultivation sector and the Economic forest product sector emerge as the leading sectors, outperforming the national growth effect by 7.18 and 1.02 times, respectively. In the secondary forestry industry, the Wood processing sector, the Furniture manufacturing sector, the Wood paper-making sector, and the Non-timber forest product sector mostly show the Progressive types as the leading sectors from 2006 to 2020. The tertiary industry’s regional types fluctuate from the Progressive type to the Medium type, with the Forestry tourism sector, the Ecological service sector, and the Non-forestry service sector displaying leading industry characteristics. Recommendations for enhancing inter-sector integration include fostering collaborative agglomeration, unlocking the potential value of wetland resources, and strengthening cross-industry integration through e-commerce clusters to improve the forester’s e-commerce operational capabilities. Full article

The green transformation of the service sector is crucial for promoting the construction of ecological civilization and boosting high-quality development. The aim of the relationship between environmental regulation and eco-efficiency is to explore the path of the green transformation of the service sector. Based on the provincial data in China from 2001~2019, this study investigated the effect of environmental regulation on the eco-efficiency of the service sector, concentrating on the influencing mechanism and threshold effect of environmental regulation on the eco-efficiency of the largest service sector in the Chinese economy. Applying the panel OLS model, mediating model, and threshold effect model. The main findings are as follows. First, environmental regulation is not beneficial for boosting the eco-efficiency of the service sector because of compliance costs. Second, environmental regulation can reduce the eco-efficiency of the service sector through service agglomeration. However, the effect of innovation compensation can hinder the negative influence of environmental regulation on the eco-efficiency of the service sector. Third, the inhibiting effect of environmental regulation disappears non-linearly with boosting economic development. Conversely, the positive influence of environmental regulation becomes negative, along with improving service agglomeration. At last, this study provided recommendations for the policymakers who hope to accelerate the green transformation of the service sector from the perspectives of strengthening technological innovation, reducing agglomeration, and establishing coordinated development mechanisms. Full article

This study explores the spatial and temporal evolution of Paomo restaurants in Xi’an’s central urban area from 2012 to 2023, utilizing point-of-interest (POI) data and advanced analytical techniques. Employing methods such as nearest neighbor distance, kernel density estimation, and band set statistical analysis, we have systematically analyzed the shifting patterns and critical factors influencing the distribution of these restaurants. The study’s findings reveal a significant outward expansion from the city center to the periphery, marked by substantial spatial agglomeration and the emergence of multiple distinct cores. This distribution shift is largely driven by urban policy initiatives and market dynamics. Key factors identified include accessibility to transportation, residential living standards, and the availability of commercial services. These factors highlight the substantial impact of urban spatial reorganization and socioeconomic factors on the specialty catering sector. The insights from this study provide valuable implications for urban planning and policy-making, suggesting targeted strategies for the spatial arrangement of specialty catering services to enhance urban layouts and promote sustainable economic development. Full article

Elucidating the impacts of service industry’s agglomeration on the optimization of industrial structures holds paramount significance in advancing urban economic growth and fostering the coordinated and sustainable development of city economies. This study leverages panel data encompassing 251 prefecture-level cities spanning from 2003 to 2019, employing a spatial Dubin model to scrutinize the influence of distinct types of service industry agglomeration on industrial structure optimization. The results show that specialized agglomeration within the service sector significantly inhibits the rationalization of industrial structures and their underlying fundamentals. Conversely, heightened levels of agglomeration in diversified service industries facilitate the rationalization of industrial structure, predominantly driven by regional spatial spillover effects. Further analysis reveals heterogeneity in service industry agglomeration across cities of varying sizes concerning industrial structure optimization, notably accentuating underutilized spatial spillover effects in smaller cities. In light of these insights, this paper advocates for cities to capitalize on the agglomeration and spillover effects between the service industry and other sectors, strategically selecting optimal service industry agglomeration modes to propel industrial structure optimization. Full article

Knowledge-intensive business services (KIBSs) are key links in leading the sustainable development of cities. Studying the spatial pattern and influencing factors of urban KIBSs can help improve the utilization of KIBS resources. Taking the Wuhan metropolitan area as a case study, based on data from industrial and commercial registration enterprises, this study uses the multi-ring buffer zone analysis and kernel density estimation method to analyze the spatial pattern of KIBS, and uses a negative binomial regression model to detect the influencing factors of the spatial pattern of KIBS. The results show that: (1) KIBSs are mainly distributed in the inner suburbs, presenting a multi-center spatial pattern, exhibiting the law of agglomeration along entrepreneurial streets, headquarter bases, science and technology parks, university clusters, business centers, and industrial bases. Obvious differences exist in the spatial patterns of KIBS sub-sectors. (2) Land price, traffic conditions, office space, commercial environment, technology factors, industry diversity, incubation environment, investment environment, manufacturing foundation, agglomeration factors, and policy factors are the main factors affecting the spatial patterns of KIBSs. There are differences in the impact of influencing factors on KIBS sub-sectors. The results can provide a decision-making basis for the rational layout and planning of urban KIBSs in the post-industrial era. Full article

In the context of escalating global concerns for “carbon neutrality and peak carbon” and the urgent need for ecological conservation, deciphering the spatiotemporal interactions between carbon emissions and the ecosystem service value (ESV) in relation to land use changes becomes critically significant. Identifying areas to bolster ecosystem services and curtail carbon emissions, especially within the Guanzhong urban agglomeration, is crucial for advancing sustainable and low-carbon regional development. The study focuses on the urban agglomeration of Guanzhong, using land use and socio-economic data from three periods between 2010 and 2020. Methods such as grid analysis and bivariate spatial autocorrelation models are employed to explore the temporal and spatial evolution characteristics and interaction patterns of carbon emissions and ESV in relation to land use. The findings reveal: (1) during 2010–2020, the Guanzhong urban agglomeration experienced varied transitions in land use types, marked by a significant net decrease in arable land and net increases in grasslands and urban construction areas. (2) The ESV in the Guanzhong urban agglomeration witnessed a consistent rise, exhibiting a spatial distribution pattern with higher values in the southwest and lower in the northeast. Among the categorized ecosystem service functions, services related to hydrological and climate regulation stood out. (3) The Guanzhong urban agglomeration observed an average annual growth rate of 5.03% in carbon emissions due to land use, with a spatial trend that was higher in the center and tapered towards the periphery. Predominant carbon sources included arable lands and urban construction areas, while forests accounted for 94% of carbon sequestration. (4) A pronounced negative correlation between the ESV and carbon emissions was discerned in Guanzhong. Regions with a stronger correlation were primarily centered in Guanzhong, notably around Xi’an and Baoji. The results emphasize the pivotal role of the primary sector’s qualitative development in harmonizing the ESV and carbon emission dynamics in the Guanzhong urban agglomeration. This research provides valuable insights for optimizing land resource management, aligned with the rural revitalization strategy, streamlining carbon dynamics, bolstering ESV, augmenting carbon sequestration efficiency, and guiding ecological spatial planning. Full article

The resource-based city is a type of city with pronounced ageing problems. Correctly grasping the socio-economic adaptability to ageing in resource-based cities can help actively guide the direction of urban development and build a new socio-economic order for the elderly. This paper first selects 125 resource-based cities in China from 2000 to 2020 for characteristic analysis, and finds that resource-based cities are characterized by fast growth rate and a large proportion of ageing population, deep ageing, large regional differences and uncoordinated ageing development with regional socio-economic development levels. The research objective of this article is to explore the temporal evolution characteristics, spatial patterns, agglomeration characteristics, and factors hindering the socio-economic adaptability to ageing in resource-based cities in China from 2000 to 2020. Therefore, an indicator system for assessing the socio-economic adaptability to ageing was constructed, and the improved entropy-weighted TOPSIS model was used to measure the socio-economic adaptability to ageing in 113 resource-based cities in China from 2000 to 2020. The spatio-temporal variation characteristics of the socio-economic adaptability to ageing in resource-based cities were analyzed by descriptive analysis and Moran’s index, and the main obstacle dimensions and obstacle indicators were clarified by the obstacle factor model. The findings can be summarized as follows: Temporal Analysis: Over the timeframe assessed, the socio-economic adaptability of ageing in resource cities demonstrates a consistent year-on-year improvement. The spatial clustering pattern exhibits a noteworthy trend of “significant-significant-insignificant”. Spatial Pattern: Examining the spatial arrangement from 2000 to 2020, areas with medium-low and low adaptability are prominently concentrated in Eastern China and Northeastern China, while regions with medium-high and high adaptability are focal in Northern China and Eastern China. Hebei Province and its neighboring provinces consistently display H-H clustering, contrasting with the Southwestern regions that persistently exhibit L-L clustering. Obstacle Degree Analysis: Across the 2000 to 2020 period, dimensions related to economic development, social participation, and social security consistently emerge as the primary obstacles. Notably, the top 12 average annual obstacle indicators are selected, and within economic development dimension all 4 indicators predominate as the key obstacles. Within the social participation dimension, tertiary sector value added and total retail sales of consumer goods per capita feature as primary obstacles. Regarding social security, impediments are primarily associated with the ratio of Medicare coverage, the ratio of pension coverage, alongside the development level of the social security sector. In the domain of service provision, key obstacle indicators include park area per capita, number of books in public libraries per 100 inhabitants, and public trams and buses per 10,000 inhabitants. Full article

The data from 285 prefecture-level cities in China are selected as research samples from 2005 to 2021, using the panel data of listed companies. The empirical study examines the impact of regional industrial agglomeration levels on enterprise innovation sustainability and its heterogeneity effects. The findings reveal that industrial agglomeration in the manufacturing sector significantly hampers enterprise innovation sustainability, while agglomeration in the producer services sector promotes it. Mechanism analysis demonstrates that industrial agglomeration affects enterprise innovation sustainability through the micro-conductive mechanism of financial constraints. Heterogeneity analysis shows that the impact of manufacturing agglomeration on enterprise innovation sustainability is more pronounced in technology-intensive and high-end technology industries, whereas the impact of producer services agglomeration varies significantly in knowledge-intensive and resource-intensive industries. Furthermore, heterogeneity analysis suggests that the influence of industrial agglomeration on enterprise innovation sustainability varies according to different firm characteristics. These research findings contribute to a deeper understanding of the microeconomic effects of industrial agglomeration and expand the research perspective on the internal mechanisms and external factors driving sustainable corporate innovation. Full article