Industrial Agglomeration Research Articles

The Effect of Intelligent Development on Green Economy Efficiency: An Analysis Based on China’s Province-Level Data

As the main driving force of the new technological revolution, intelligent development is the key to promoting high-quality economic development. This paper empirically examines the nonlinear influence of intelligent development on green economy efficiency and its action paths using provincial panel data of China from 2009 to 2021. The result provides significant evidence of a U-shaped relationship between intelligent development and green economy efficiency, indicating that intelligent development initially leads to green economy efficiency decreases before ultimately increasing. Additional analysis confirms that environmental regulation, green finance, and industrial agglomeration positively moderate the impact of intelligent development on green economy efficiency. Furthermore, heterogeneous tests reveal that in the eastern region and after the release of “Made in China 2025” in 2015, the nonlinear effect of intelligent development on green economy efficiency is more pronounced. The findings of this paper provide a beneficial reference for how to leverage intelligent technology to release new kinetic energy for green economic growth under the new development concept.

Spatiotemporal characteristics and influencing factors of non-CO2 greenhouse gas emission intensity from China's livestock sector.

The livestock sector is a major source of non-CO2 greenhouse gas (GHG). As China has the world's largest livestock production, analyzing factors influencing GHG emission intensity of livestock (GEIL) is crucial for guiding its emission reduction policies. Yet, most current studies on livestock GHG focus on emission amount (GEAL) over GEIL, neglecting comprehensive utilization of spatial econometric models. Furthermore, the influencing factors mainly focus on population, economic level, urbanization, and industrial structure, while the exploration of factors such as technological innovation, consumers' age structure, and industrial agglomeration remains insufficient. This study first applied the standard deviation ellipse (SDE) model to describe the spatiotemporal evolution of GEAL and GEIL across 31 Chinese provinces from 2006 to 2022. Then, using extended STIRPAT theory, we analyzed GEIL's influencing factors with exploratory spatial data analysis (ESDA) and the geographically and temporally weighted regression (GTWR) model. The results showed that: (1) GEAL was concentrated in resource-rich grassland pastoral and grain-producing areas. Provincial GEAL trends varied, while national GEAL declined rapidly and then fluctuated slightly. Cattle, pigs, and sheep/goats contributed the most to GEAL. (2) GEIL significantly decreased nationwide and provincially, with Tibet having the highest reduction rate but still leading in GEIL. (3) GEAL followed a northeast-southwest distribution, whereas GEIL exhibited an east (slightly north) to west (slightly south) pattern. (4) Spatial analysis revealed significant GEIL clustering, with higher values in western provinces and lower in central and eastern regions. (5) Population scale, urbanization, age structure, proportion of ruminant animal production value, location quotient of livestock, employment income per capita of agricultural personnel, patent-granted intensity, and educational level in rural areas had diverse spatiotemporal impacts on GEIL across provinces. Tailored emission reduction policies and regional collaborative governance are recommended.

Can crop production agglomeration reduce carbon emissions?—empirical evidence from China

Reducing carbon emissions is crucial for environmental protection and the survival of humankind, particularly in agricultural growth, as it ensures the sustainability of the food supply. This study examines the import of the crop production agglomeration on carbon emissions across several areas of China. It employs panel data spanning from 2012 to 2022. The crop production agglomeration was assessed using the average industrial agglomeration rate, whereas the carbon emissions were evaluated using the IPCC carbon emission factors. Empirical analyses were conducted using the panel fixed effects model and the Spatial Durbin Model . The results indicate that crop production agglomeration directly reduces carbon emissions. Moreover, the concentration of crop production has a geographical demonstration effect on carbon emissions, where greater levels of crop production agglomeration result in a more efficient decrease of carbon emissions in nearby regions. An analysis of heterogeneity indicates that the impact of crop production agglomeration on carbon emissions is more pronounced in the eastern and northeastern regions of China compared to the central and western areas. The study advocates for the formulation of tailored carbon reduction methods that align with the distinct attributes of crops in various locations. It promotes variety and low-carbon development in crop production to drive industrial advancement. The study advocates for enhancing cooperation among crop production enterprises across various areas to provide platforms for information exchange and technical innovation. Furthermore, it advocates for governments to design efficient methods and regulations to reduce carbon emissions in crop production.

Urban Digital Transformation, E-Commerce and Rural Revitalization

Urban digital transformation has become important to promote social progress and economic growth. At present, rural development is seriously lagging, and it is of great significance to explore the impact of urban digital transformation on rural revitalization to promote the coordinated development of urban and rural areas and rural revitalization. This study empirically investigates the impact of urban digital transformation on rural revitalization based on municipal panel data from 2009-2021. The study shows that urban digital transformation significantly drives rural revitalization, and e-commerce plays a mediating role in it. The improvement of green economy efficiency, industrial agglomeration, and human capital level will enhance the impact of urban digital transformation on rural revitalization. From the results of heterogeneity analysis, the effect is more obvious in the eastern region. This study also proposes policy suggestions from three perspectives: to promote urban digital transformation, to focus on key elements such as e-commerce, and to develop regional synergies, which provide a reference for promoting rural revitalization in the context of urban digital transformation.

Spatio-temporal Characteristics and Influencing Factors of Carbon Sources/Sinks in the Yangtze River Delta Under Carbon Neutrality Target

Clarifying the dynamic relationship between carbon sources/sinks and urban development is crucial to achieving carbon neutrality and sustainable development in the region. This study used net ecosystem productivity （NEP） to evaluate the carbon sinks of the terrestrial ecosystem in the Yangtze River Delta （YRD） from 2005 to 2020. Geodetectors were used to explore the impact of urban expansion, socio-economic factors, and their interactions on carbon sources/sinks in the YRD and to evaluate the gap in achieving carbon neutrality in cities. The results showed that： ① In 2005-2020, the NEP contribution of the YRD was 69.13 Mt·a-1 and the loss of carbon sinks due to urban expansion was 1.97 Mt. The carbon emissions offset by carbon sinks were approximately 3% to 6%. ② The high carbon source areas were mainly distributed in the central and eastern YRD and the high carbon sink areas were primarily distributed in Zhejiang Province and southern Anhui Province. The forest-rich cities of Huangshan and Lishui, with smaller gaps between carbon emissions and carbon sinks, had the greatest potential for achieving carbon neutrality. Industrial or resource-based cities such as Xuzhou and Maanshan faced greater pressure to achieve carbon neutrality. Developed cities such as Shanghai and Suzhou had the largest carbon neutrality gap. ③ Urban expansion, economic and industrial agglomeration, high-density distribution of the population, and compact urban form were the key variables leading to the widening gap between carbon emissions and carbon sinks in the YRD. The increase in ecological space could effectively reduce carbon emissions. Urban expansion and socioeconomic development could be coupled with each other and have significant synergistic effects on carbon emissions.

MARKET-INCENTIVIZED ENVIRONMENTAL REGULATION, INDUSTRIAL AGGLOMERATION AND THE EFFECTS ON CARBON SHADOW PRICE: EVIDENCE FROM CHINA’s LOCAL PILOT POLICIES FOR CARBON MARKET CONSTRUCTION

This study evaluates the effectiveness of market-driven environmental policies in fostering sustainable, low-carbon development through the lens of China’s pilot carbon market policies, launched in 2013. Utilizing data from 283 prefecture-level cities from 2007 to 2019, we apply a difference-in-differences methodology to explore the Convergence of Carbon Shadow Price (CSP) and to assess potential spatial spillover effects. Our findings robustly demonstrate that the pilot carbon market policies significantly foster the CSP across pilot cities. This convergence is particularly pronounced in eastern regions and major cities, facilitated by advanced technological integration. Furthermore, our results reveal adverse spatial spillovers, as nearby non-pilot areas suffer competitive disadvantages, akin to the beggar-thy-neighbor effect. The study also illustrates that industrial agglomeration affects CSP in a U-shaped manner, but synergistic agglomerations of manufacturing and service sectors enhance regulatory impacts significantly.

Will land transfer affect agricultural carbon emission efficiency? An empirical study based on provinces in China.

Studying the carbon emission efficiency of the agricultural sector plays an important role in achieving the goal of global carbon neutrality. As a common economic behavior in agricultural production, it is still unclear about that how land transfer affects agricultural carbon emission efficiency (ACEE). Therefore, according to panel data of 30 provinces (including autonomous regions and municipalities at the same level) from 2005 to 2019 in the Chinese mainland, this work adopts the panel Ordinary Least Square (OLS) to reveal the influential mechanism of land transfer on ACEE. Subsequently, the panel quantile regression and sub-sample regression are applied to divide the provinces into different types of regions, so as to explore what kind of heterogeneity effect would occur, if land transfer acts upon ACEE. Finally, this work discovers the influential mechanism of land transfer to ACEE by agricultural industrial agglomeration and agricultural land management scale. The research results support the view that the land transfer significantly inhibits the improvement of ACEE, and also prove that there is regional heterogeneity in the impact of land transfer on ACEE, and this heterogeneity is mainly reflected in the impact intensity of land transfer on ACEE in different regions. The more important and deeper discovery is that agricultural industrial agglomeration and agricultural land management scale intensify the inhibition of land transfer on ACEE. Considering that both agricultural industrial agglomeration and agricultural land management scale have a regulatory effect on the influence of the land transfer on ACEE, this work suggests that the government improve the land transfer market system, implement differentiated agricultural management measures, and reasonably promote agricultural industrial agglomeration to gradually realize economies of scale.

Study on the Economies of Scale Effect of Biomedical Industry Agglomeration in the Yanatze River Delta

As one of the most active areas in China's economic development, the biomedical industry gathers and develops here, and gradually forms a competitive industrial cluster. As one of the important characteristics of industrial agglomeration, economies of scale effect is of great significance for enhancing industrial competitiveness and promoting regional economic development. This study conducted an in-depth analysis of the economies of scale effect of the biomedical industry agglomeration in the Yangtze River Delta, explored the specific manifestation and causes of the economies of scale effect by sorting out the current situation of industrial agglomeration, and further analyzed the impact of technological innovation, cost saving, market expansion, brand effect, industrial chain synergy and policy support on industrial agglomeration. It is found that the agglomeration of biomedicine industry in the Yangtze River Delta has significantly promoted the expansion of industrial scale and the improvement of efficiency, and has a positive effect on regional economic development.

Analysis of the characteristics and influencing factors of water ecological security in Beijing-Tianjin-Hebei region: A perspective of industrial agglomeration

Water ecological security (WES) is crucial for promotion of economic and social development characterized by high-quality, while exploring the characteristics of WES and its response mechanism to industrial agglomeration can help formulate and implement relevant policies. In this study, a WES evaluation index system tailored for the Beijing-Tianjin-Hebei (BTH) region was constructed based on the Pressure-State-Response (PSR) framework. Additionally, building on the assessment of the degree of industrial agglomeration within the BTH region, the research employs a spatial Durbin model to investigate the impact of industrial agglomeration on WES in the BTH region. The main results are as follows: (1) The level of WES in the BTH region has steadily increased from 2005 to 2021, consistent with the trend of industrial agglomeration in the study area. (2) Influenced by regional economic development and natural conditions, the WES in the BTH region exhibits significant regional disparities and spatial autocorrelation, with levels decreasing from the central area to the east, northwest, and south. (3) Industrial agglomeration enhances WES by improving resource utilization efficiency, reducing pollutant emissions, and providing regional governments with financial resources to improve public services and water ecological protection, leading to better water ecological quality. (4) The impact of different industries on WES varies. As the proportion of the primary industry in the BTH region is relatively small, the agglomeration of the secondary and tertiary industries is more effective in promoting improvements in WES. Moreover, the spatial spillover effects of industrial agglomeration in the BTH region are significant. To enhance the level of WES in the region, this study recommends strengthening water ecological protection, promoting industrial structure adjustment, and enhancing collaborative development.

Factors Influencing Industrial Agglomeration in Indonesia

This study analyzes the factors affecting industrial agglomeration in Indonesia using panel data from 34 provinces during the 2012-2023 period. The analytical methods employed include Location Quotient (LQ) and panel data regression, with testing results indicating that the Fixed Effect model is the most appropriate. The findings reveal that economic growth and investment have a negative and significant impact on industrial agglomeration, while the Human Development Index (HDI) shows a negative but insignificant effect. On the other hand, population size has a positive and significant influence on agglomeration.

Achieving inclusive urbanization through county-led industrial specialization: from the perspective of rural labor supply

PurposeThe role of intermediate urban centers (IUCs) (like towns/small cities/rural counties) between large cities and villages in leading urbanization has been increasingly highlighted. This article conceptually and empirically examines what types of industrial agglomeration (specialized or diversified agglomeration) of rural county in China would provide a more inclusive and efficient local labor market for rural workers to be inclusively urbanized.Design/methodology/approachWe employ a three-stage labor supply model to theoretically generalize the decisions of rural workers to migrate to large city, to undertake nonfarm jobs in rural counties and the length of nonfarm jobs. Then, combining three national-level datasets of China (National Economic Census (NEC), China Household Income Project (CHIP) and the China county statistical yearbook), we explore how the type of industrial agglomeration in a rural county affects the labor supply decision of rural workers.FindingsWe show that rural workers in rural counties with higher level of industrial specialization are more likely to: (1) work locally than migrating to large cities; (2) find nonfarm jobs by themselves instead of relying on social networks (3) find a more stable and formal nonfarm job. These effects are significant when rural counties specialize in industries that they have local comparative advantages compared to large cities. This work provides insights into achieving inclusive urbanization by developing industry specialization in rural counties in China, which may also be useful for other developing countries.Originality/valueFirst, the conceptual framework of this paper extends the basic model of rural-urban migration longstanding in the literature, to a multi-choice and three-stage labor supply model. Second, we empirically quantify the industrial structure of rural counties using unique dataset, which has been emphasized in the development economics literature but neglected due to data limitations. Third, this paper expands our understanding of the role of rural counties in inclusive urbanization and contributes to the future development of the county-led urbanization policies.

The correlation evolution and formation mechanism of energy ecological efficiency in China: A spatial network approach

Enhancing energy ecological efficiency is critical for addressing climate change and advancing green, low-carbon economic development. This study utilizes an innovative Super Epsilon-based Measure (EBM) model alongside a gravity model to evaluate the energy ecological efficiency across 30 provincial-level regions in China from 2011 to 2021. This research fills a critical gap in the literature by combining efficiency assessments with complex network analysis, offering a novel perspective on the spatiotemporal evolution and distribution of energy efficiency. The findings reveal that: (1) China's energy ecological efficiency exhibited a fluctuating growth trend over the study period, with significant regional disparities characterized by the pattern “eastern region > central region > western region.” (2) Overall network density, network correlation, and the number of network relationships maintaining relatively stable growth, with a growth rate of 28.93 %, 27.51 % and 28.89 %, respectively. However, the efficiency network's overall connectivity remains low, with fewer correlation lines in the western and central regions and higher network density in the eastern coastal areas. (3) Quantitative analysis using the Quadratic Assignment Procedure (QAP) method identifies key factors influencing network formation, such as urban openness, industrialization level, environmental regulation, economic development, geographical proximity, population quality, and resource endowment. Notably, every 1 % increase in the industrial agglomeration level will have a negative impact of −0.248 % on the energy ecological efficiency network. This research fills a gap in understanding the structural and dynamic aspects of China's energy ecological efficiency, offering novel insights that can guide policymakers in formulating strategies to enhance sustainable development.

Greening the marine map: a comprehensive study of China’s marine ecological and economic synergy

IntroductionThis research examines the coupling coordinated development dynamics between marine ecological governance and marine economic development in China’s coastal provinces. The marine ecological governance and economic development data of China’s 11 coastal provinces were comprehensively analyzed from 2011 to 2021.MethodsWe established a multi-dimensional evaluation index system for marine ecological governance and economic development, employing the global entropy weight method for quantification. Furthermore, the research examines and analyzes the trends in coordinated development and divergence between these two systems by constructing a coupled coordination degree(CCD) model, utilizing the Theil index decomposition method, and employing the geodetector detector.ResultsThe Northern marine economic circle outperforms the Eastern and Southern ones regarding marine ecological governance, while the Eastern marine economic circle is the most advanced in marine economic development. The results of the CCD model indicate that Guangdong, Shandong, Hebei, and Jiangsu have the best coupling coordinated development, while Hainan, Tianjin, and Guangxi are at the bottom. The results of the Theil index decomposition method indicate that intra-regional disparities are the primary cause of coupling coordinated regional development variations. At the same time, the results of the geographic detector further substantiate that energy structure, degree of industrial agglomeration, and foreign overseas direct investment are the principal influencing factors.DiscussionThe study presents policy recommendations derived from the findings intended to foster the integrated advancement of marine ecology and economy in coastal provinces, reduce regional development disparities, and establish a scientific foundation for achieving a sustainable marine economy.

Logistics Industry Agglomeration Affects New-Type Urbanization—An Empirical Test Based on Spatial Panel Models

The rapid development of the logistics industry and its cooperation with other production factors have an impact on the promotion of new-type urbanization (NTU), a more sustainable and inclusive model of urban growth. This article analyzes in-depth the impact mechanism of logistics industry agglomeration, a new way to promote innovation and improve industrial efficiency, on NTU from various dimensions, including population, economy, society, space, ecology, and urban-rural coordination. Using panel data from 31 provinces in China between 2002 and 2021, the level of NTU was calculated using the entropy weight method, and a spatial econometric model was employed to examine the impact of logistics industry agglomeration on NTU and its spatial spillover effects. The research findings are as follows. The study reveals a spatial correlation between logistics industry agglomeration and NTU across Chinese regions. Logistics industry agglomeration significantly enhances NTU levels and generates spatial spillover effects, facilitating urban development in surrounding areas. The influence of regional logistics industry agglomeration on NTU demonstrates notable spatial heterogeneity, with the central region exerting a greater impact than the eastern and western regions. It can help in the understanding of regional differences and help policy makers optimize resource allocation. The impact of logistics industry agglomeration on NTU is nonlinear, primarily characterized by a substantial improvement in NTU at the medium and low levels due to logistics industry agglomeration. It helps identify regional differences and supports personalized decision making. The level of human capital, the degree of government intervention, the degree of marketization, the level of technological industrialization, the ratio of elderly dependency, and the level of innovation have direct effects and spillover effects on NTU. This paper elaborates on the mechanism of logistics industry agglomeration on NTU in China and proposes targeted proposals for promoting the development of NTU, which is highly important for promoting urbanization in other regions of the world from the perspective of industry.

Spatial and Temporal Evolution of the Coupling of Industrial Agglomeration and Carbon Emission Efficiency—Evidence from China’s Animal Husbandry Industry

Drawing upon the data of China’s animal husbandry industry from 2000 to 2020 in 30 provinces, an EBM model incorporating non-desired outputs was employed to gauge the carbon emission efficiency of the animal husbandry industry. Coupling degree models, spatial autocorrelation models, and Markov chain models were utilized to assess the coupling degree between the industrial agglomeration of the animal husbandry sector and its carbon emission efficiency, and to analyze its spatio-temporal distribution and evolution. The outcomes showed that (1) the coupling degree of China’s animal husbandry industry agglomeration and carbon emission efficiency exhibited an overall downward inclination. Notably, the diminishing tendency of the coupling degree was more pronounced in the eastern, central, and western parts of the country; (2) the coupling degree of the 30 provinces showed a spatial distribution of “western > central > northeast > eastern”; (3) the coupling degree showed obvious agglomeration distribution characteristics, wherein a substantial quantity of provinces was located in high–high clustering zones and low–low clustering zones; (4) the coupling degree of various provinces remained fairly stable, but after considering the spatial and geographical correlation, the coupling degree of each province would be influenced by the coupling degree of its adjacent provinces. Evidently, there remained a substantial scope for the enhancement of the coupling coordination degree between the industrial agglomeration of China’s animal husbandry and the carbon emission efficiency. This research is capable of furnishing a theoretical allusion for promoting regional cooperation, leveraging agglomeration advantages, and implementing carbon emission abatement regimes and directives to enhance the low-carbon development level of animal husbandry industry agglomeration in China.

Multi-dimensional minimum cost consensus model and its application in the location problem of industrial agglomeration zone

The current minimum cost consensus theories predominantly focus on one-dimensional opinions, thereby limiting their capacity to address increasingly complex decision-making scenarios. Consequently, this paper extends the one-dimensional minimum cost consensus model (MCCM) to the multi-dimensional minimum cost consensus model (MDMCCM), defining the consensus level and satisfaction level under multi-dimensional opinions. The consensus level depicts the degree of concentration of different opinions and their relative proximity to the consensus opinion, thus ensuring the quality of consensus. The satisfaction level describes the degree of psychological acceptance of decision-makers (DMs), ensuring the stability and efficiency of consensus. Subsequently, the MDMCCMs considering both consensus level and satisfaction level under Euclidean distance and Manhattan distance are proposed, respectively. Following this, a comparative analysis between the new models proposed in this paper and other existing models is provided, and the consensus-reaching framework and algorithm under different distance measures are constructed. Finally, a novel site selection consensus mechanism based on MDMCCMs is proposed and applied to the consensus problem of site selection for industrial agglomeration zones in suburbs and urban areas, thus verifying the rationality and effectiveness of the models proposed in this paper. Additionally, a comparative analysis of results and a sensitivity analysis of parameters are provided.

Trade liberalization and export performance in China: Export-margin approach with firm-level data in 1995–2019

A key challenge facing developing countries is how to promote export performance through trade liberalization (TL). Using large and highly disaggregate firm-level data with 6-digit HS categories for 1995–2019, we investigate the issue for China based on the recent literature of export margins (XM). Export growth is decomposed into extensive and intensive margins (EM and IM), and IM further into price and quantity margins (PM and QM). We develop three empirical hypotheses based on a theoretical model that includes external economies of scale (EES) and industrial agglomeration (IA) as well as TL-XM links. Then we take China's entry to World Trade Organization (WTO) in 2001 as a quasi-natural experiment in difference-in-difference (DID) regressions. The estimate results suggest that TL increases both EM and IM (and QM) but reduces PM after China's entry to TWO in 2001. The finding is robust to various specifications of the empirical model and measurements of the variables. The further estimations show significantly positive effects of EES, IA, and their interactive terms with TL on EM, IM, and QM. The positive effects are larger for medium-tech industries than low-tech and high-tech industries.

Digital industry agglomeration, resource dependence and urban green economy efficiency

ABSTRACT Under the background of booming global digital industries and green transformation, this study revisits the impact of digital industry agglomeration on urban green economy efficiency, focusing on the threshold effects of resource dependence. Taking 277 cities in China as an example, the results from spatial models indicate that when resource dependence is lower than a certain value, digital industry agglomeration improves urban green economy efficiency. However, once the degree of resource dependence crosses this threshold, digital industry agglomeration can’t enhance urban green economy efficiency significantly anymore. In addition, the estimated threshold value of resource dependence is higher in cities with stronger environmental regulation, higher green technological quality, and sounder digital infrastructure. Accordingly, this study recommends reducing the dependence of related industries on natural resources and improving environmental regulation, green technological quality, and digital infrastructure. This study is beneficial to explain those cities with developed digital industries and low green efficiency, and further explores how the threshold value of resource dependence changes with different situations, which addresses the shortcomings of existing research.

Economic Growth in Central Java: Does Manufacturing Industry Agglomeration Matter?

Economic Growth in Central Java: Does Manufacturing Industry Agglomeration Matter?

Exploring the impact of digital industry agglomeration on provincial energy efficiency in China: A panel data analysis from 2012 to 2020

Using panel data from 30 Chinese provinces between 2012 and 2020, this study examines the relationship between digital industry agglomeration and provincial energy efficiency. Key findings include: (1) China's energy efficiency remains suboptimal with an average annual value of 0.525. In detail, the performance of energy utilization stage is better than that of pollution treatment stage. (2) Digital industry agglomeration has a positive impact on energy efficiency, highlighted by time-lag effect. This effect unfolds as a gradual accumulation, with varying influences across distinct regions. Furthermore, digital industry agglomeration is hard to give full scope to its efficiency promotion advantages for regions with low digital industry agglomeration level. (3) Green technological innovation and industrial structure adjustment are instrumental pathways through which digital industry agglomeration influences energy efficiency. Additionally, governmental support not only enhances this impact but also affects the intermediary roles of green technological innovation and industrial structure adjustment. (4) There are significant positive spatial spillover effects, with digital industry agglomeration boosting energy efficiency in neighboring provinces. Based on these insights, we recommend the Chinese government prioritize the development of globally competitive digital industry agglomerations to promote energy conservation, reduce emissions, and support a sustainable energy transition.