Beijing Tianjin Hebei Urban Agglomeration Research Articles

Comparison of Different Impact Factors and Spatial Scales in PM2.5 Variation

PM2.5 particles with an aerodynamic diameter of less than 2.5 μm are receiving increasing attention in China. Understanding how complex factors affect PM2.5 particles is crucial for the prevention of air pollution. This study investigated the influence of meteorological factors and land use on the dynamics of PM2.5 concentrations in four urban agglomerations of China at different scales from 2010 to 2020, using the Durbin spatial domain model (SDM) at five different grid scales. The results showed that the average annual PM2.5 concentration in four core urban agglomerations in China generally had a downward trend, and the meteorological factors and land use types were closely related to the PM2.5 concentration. The impact of temperature on PM2.5 changed significantly with an increase in grid scale, while other factors did not lead to obvious changes. The direct and spillover effects of different factors on PM2.5 in inland and coastal urban agglomerations were not entirely consistent. The influence of wind speed on coastal urban clusters (the Pearl River urban agglomeration (PRD) and Yangtze River urban agglomeration (YRD)) was not significant among the meteorological factors, but it had a significant impact on inland urban clusters (the Beijing–Tianjin–Hebei urban agglomeration (BTH) and Chengdu–Chongqing urban agglomeration (CC)). The direct effect of land use type factors showed an obvious U-shaped change with an increase in the research scale in the YRD, and the direct effect of land use type factors was almost twice as large as the spillover effect. Among land use type factors, human factors (impermeable surfaces) were found to have a greater impact in inland urban agglomerations, while natural factors (forests) had a greater impact in coastal urban agglomerations. Therefore, targeted policies to alleviate PM2.5 should be formulated in inland and coastal urban agglomerations, combined with local climate measures such as artificial precipitation, and urban land planning should be carried out under the consideration of known impacts.

Financial efficiency and financial resource allocation of Beijing–Tianjin–Hebei urban agglomeration

This study focuses on improving financial efficiency and rationally allocating financial resources in the Beijing–Tianjin–Hebei, based on data from 2011 to 2019. Firstly, the Slack-Based Measure (SBM) model based on Shannon Entropy is adopted to measure financial efficiency in Beijing–Tianjin–Hebei. Secondly, a Beijing–Tianjin–Hebei inter-regional financial resource allocation scheme is proposed using the Generalized Equilibrium Efficient Frontier Data Envelopment Analysis (GEEFDEA) model. Finally, the external environmental factors influencing financial efficiency are explored using spatial and other econometric models. Efficiency measurement reveals that financial efficiency in Beijing–Tianjin–Hebei is unevenly developed. The financial efficiency of Beijing, Tianjin, and Zhangjiakou is high and stable, whereas the financial efficiency of Tangshan, Langfang, and Shijiazhuang is high, but fluctuates significantly. In comparison, the financial efficiency of other cities is low. The factors and the adjustment amount to improve urban financial efficiency are obtained by studying the allocation of financial resources. And it is found that Tianjin and Tangshan have more financial resource surpluses than other cities. By exploring influencing factors, it is found that the financial development level, innovation level, and infrastructure construction level of Beijing–Tianjin–Hebei significantly affect financial efficiency.

Quantification of Urban Heat Island Effect and Differences in Regional Influence Based on Footprint Analysis: A Case Study of the Beijing–Tianjin–Hebei Urban Agglomeration

Quantification of Urban Heat Island Effect and Differences in Regional Influence Based on Footprint Analysis: A Case Study of the Beijing–Tianjin–Hebei Urban Agglomeration

Study on the Trade-Offs of Land Functions in the Central Plain of China for Sustainable Development

Properly managing the relationship between food security, ecological protection, and urbanization, and coordinating the trade-offs among these three factors for land demand are extremely important for environmental management and sustainable development. In this study, we attempt to analyze the state of land use trade-offs from a dynamic perspective in terms of both potential and efficiency. We have innovatively proposed a new land use trade-off analysis framework integrating the Estimation System for Land Productivity (ESLP) model, machine learning algorithms, ecosystem service value assessment, and spatial analysis method. By applying the framework, the potential and efficiency of the three land use functions of urban development, ecological protection, and agricultural production on the Huang-Huai-Hai (HHH) Plain were comprehensively estimated, and the trade-off relationship between the three land use functions was identified. The results showed a prominent conflict between urban development and agricultural production (around 8%) on the HHH Plain, especially in the Beijing–Tianjin–Hebei urban agglomeration and the southern Jiangsu urban agglomeration. In the mountainous areas, such as northern Hebei and central Shandong, there was an obvious trade-off between ecological land and agriculture land. Most cities had a trade-off between ecological land and urban land (approximately 6% of the study area), but it was relatively more relaxed in comparison. Finally, we found that on the HHH Plain, where land resources are fiercely competitive, spatial planning and land resource control depend not only on the suitability or potential of the land unit, but also on whether the efficiency of land use has reached an appropriate range. The smart way to use land resources is to scientifically trade-off different land use functions and improve the efficiency of land use to achieve maximum benefit.

Assessment of urban resilience and subsystem coupling coordination in the Beijing-Tianjin-Hebei urban agglomeration

Building resilient cities is a novel concept for dealing with risk crises and achieving sustainable urban development in modern times. This paper utilized data from the Beijing-Tianjin-Hebei urban agglomeration (BTHUA) spanning from 2009 to 2019, along with the CRITIC-Entropy weight method, coupling coordination degree (CCD) model and spatial correlation analysis to measure urban resilience and investigate subsystem coupling coordination relationships while considering spatial clustering characteristics. The results indicate that: (1) Spatially, there is a significant gap between the UR index of cities in Hebei Province and that of Beijing-Tianjin, with a “Beijing-Tianjin dual-core driven” trend. Regarding temporal evolution, the growth trend of the UR index is consistent with that of the subsystem resilience index. (2) In general, the CCD of the urban resilience subsystem is slowly trending upwards, with the overall CCD level of BTHUA ranging from primary coordination to moderate coordination. (3) The degree of spatial correlation of subsystem CCD in BTHUA is weak, and more cities exhibit heterogeneous agglomeration. These findings provide valuable inspiration and reference for resilient city construction and coordinated regional development to a certain extent.

Evolution Characteristics and Main Influencing Factors of Carbon Dioxide Emissions in Chinese Cities from 2005 to 2020

Based on the carbon emission database of the China Urban Greenhouse Gas Working Group, this paper analyzed the spatiotemporal evolution characteristics and main influencing factors of urban carbon dioxide emissions in China using ArcGIS spatial analysis and SPSS statistical analysis methods, in order to provide a reference for the formulation of the national “double-carbon” strategy and the construction of low-carbon urbanization. The results showed that (1) the urban carbon dioxide emissions in China exhibit a “point-line-area” spreading spatial grid. Carbon dioxide emissions form a planar emission pattern surrounded by the Beijing–Tianjin–Hebei urban agglomeration, Yangtze River Delta urban agglomeration, and Central Plains urban agglomeration. A high per capita and high-intensity emission belt from Xinjiang to Inner Mongolia has been formed. (2) The proportion of industrial emissions continues to decrease, and the range of high industrial emissions has gradually crossed the “Hu Huan-yong Line”, spreading from eastern China to the whole country. The emissions from transportation, the service industry, and households have become new growth points, and high-value emissions from households have also shown a nationwide spreading trend. (3) The main factors influencing the spatial distribution of carbon dioxide emissions are urbanization, the economy, industry, investment, and household energy consumption.

Quantification and spatial pattern of embodied CO2 footprint of prefabricated buildings in urban agglomerations: A case study of Beijing–Tianjin–Hebei, China

Prefabricated buildings (PBs) boost a productive and sustainable construction industry transition and can influence the carbon footprint of the construction sector. With China's continued urbanization, an expanding PB sector is foreseeable, exerting substantial carbon dioxide (CO2) mitigation pressure. By using the Chinese Industrial Ecology Lab technology, we developed a hybrid multi-regional input‒output model that integrates a bottom-up inventory of building materials into China's 2017 economic system to quantify PB-embodied CO2 emissions in the Beijing–Tianjin–Hebei urban agglomeration. Two structural types (shear wall and frame-shear wall) with four prefabrication rates (15%, 30%, 50%, and 60%) were considered. The results showed an average of 756 kg/m2 PB-embodied CO2 emissions in the urban agglomeration, with Tianjin (488 kg/m2) and Zhangjiakou (1271 kg/m2) showing the lowest and highest values, respectively. Approximately 60% of PB-embodied CO2 emissions were generated within the urban agglomeration, and Tangshan, Handan, and Zhangjiakou were the major contributors. The PB-embodied CO2 footprint was dominated by electricity and heat generation (42%), non-metal mineral products (27%), and metal smelting and processing (15%). The results revealed the spatial pattern of PB-embodied CO2 footprint in the Beijing–Tianjin–Hebei urban agglomeration and identified key emission sectors in the PB supply chain, contributing to low-carbon PB delivery in China.

Performance of regional climate model RegCM4 with a hydrostatic or non‐hydrostatic dynamic core at simulating precipitation extremes in China

AbstractIn this study, we investigated the performance of RegCM4 with a hydrostatic or non‐hydrostatic dynamic core and different land surface models at simulating the mean and extreme precipitation during 1991–1999 in China and the Beijing–Tianjin–Hebei urban agglomeration (JJJ) in northern China. The resolutions were 12 km with the whole of China included and 3 km nested over the JJJ region. The observed daily precipitation data comprised the CN05.1 data set from China Meteorological Administration. First, RegCM4 with a hydrostatic dynamic core forced by the Max Planck Institute Earth System Model (MPI‐ESM1.2‐HR) that participated in CMIP6 was used to simulate the east Asia area at a resolution of 12 km. Next, the RegCM4 results with 12 km resolution forced RegCM4 with a non‐hydrostatic dynamic core (RegCM4‐NH) to run over the JJJ region with a 3 km convection‐permitting scale through one‐way nesting. The results showed that RegCM4 with a hydrostatic and non‐hydrostatic core could obtain more detailed seasonal and annual precipitation in China and the JJJ region relative to MPI‐ESM1.2‐HR although their performance varied over different regions. RegCM4 with the community land surface model 4.5 (CLM4.5) generally performed better at simulating the mean and extreme precipitation over the four subregions of China than that with the Biosphere–Atmosphere Transfer Scheme because of more detailed descriptions of land surface processes of CLM4.5. RegCM4‐NH with convection‐permitting resolution improved the simulations of heavy precipitation extremes over JJJ compared with RegCM4 with a hydrostatic core and resolution of 12 km. In addition, RegCM4‐NH with CLM4.5 performed better at simulating heavy precipitation extremes over JJJ compared with all other simulations, with higher correlation coefficients and larger relative root mean squared errors.

Research on Regional Carbon Emission Reduction in the Beijing–Tianjin–Hebei Urban Agglomeration Based on System Dynamics: Key Factors and Policy Analysis

Urban agglomerations are regions where the economy and population are highly concentrated, which are also spatial units with more concentrated carbon emissions. A detailed decomposition of driving factors based on changes in carbon emissions of urban agglomerations can provide a reference for better carbon reduction policies. In this paper, we establish an evaluation framework of carbon emission drivers of urban agglomeration from the perspective of CO2 generation and removal using a system dynamics method. The key influencing factors and optimal emission reduction measures of carbon emissions in urban agglomerations are explored. The results are as follows: (1) The industrial structure is the key influencing factor of carbon emissions; (2) compared with no implementation of any policies, the total carbon emissions and carbon emission intensity of integrated policies all significantly decrease, with a decrease of 43.68% and 53.32%, respectively in 2035; (3) energy structure adjustment has a significant effect in reducing carbon emissions and carbon emission intensity; (4) the role of increasing investment in technological innovation in ensuring achievement of “carbon peak” should not be ignored. It is found that integrated policies often exhibit a better emission reduction effect, but this effect is not a simple summation of the effects of each single policy.

Impact of Climate on the Carbon Sink Capacity of Ecological Spaces: A Case Study from the Beijing–Tianjin–Hebei Urban Agglomeration

Climate plays a significant role in shaping ecosystem-level carbon sinks. Research on the mechanisms of climate impacts on carbon sinks can contribute to the achievement of carbon neutrality. Investigating the mechanisms by which climate impact on carbon sinks in ecological spaces in the Beijing–Tianjin–Hebei region, one of the most important urban clusters in China, is of great significance. This study employed spatial autocorrelation and econometric models to explore how various climatic factors impact net primary productivity (which is used to represent carbon sink capacity) on a spatial scale. We found an increasing trend in NPP across the Beijing–Tianjin–Hebei urban agglomeration from 2000 to 2020, with marked spatial clustering. Climatic factors exhibited the best fit with the spatial Durbin model, except for average annual precipitation. The remaining factors had significant effects on NPP, showing spatial spillover effects. Results also showed that the average annual temperature, evaporation, and relative humidity had positive impacts on NPP at a local scale but adverse effects at a regional scale. Average annual sunshine duration and the ground temperature had negative effects on NPP locally but promoted effects regionally. Furthermore, the average annual wind speed negatively impacted both local- and regional-scale NPP. This research provides insights into how climate affects carbon sinks on a small spatial scale, offering important references for making policy decisions and improving the accuracy of carbon cycling simulations.

Developing a Multidimensional Strategy for Water Eco-Environmental Protection in the Beijing–Tianjin–Hebei Urban Agglomeration: An Integrated SWOT-PROMETHEE-AHP Approach

Developing a Multidimensional Strategy for Water Eco-Environmental Protection in the Beijing–Tianjin–Hebei Urban Agglomeration: An Integrated SWOT-PROMETHEE-AHP Approach

High-Speed Rail and Industrial Agglomeration: Evidence from China’s Urban Agglomerations

This paper explores the relationship between high-speed rail (HSR) and industrial agglomeration within urban agglomerations. The paper selects the data of the Beijing–Tianjin–Hebei Urban Agglomeration (BJHUA) and Central Plains Urban Agglomeration (CPUA) from 2002 to 2016 as the research object. The time-varying difference-in-difference (TVDID) model is innovatively applied to analyze the impact of HSR on the agglomeration of secondary and tertiary industries in urban agglomerations, and the industrial agglomeration effects of the two urban agglomerations are compared. The results show that the influence of high-speed railways on the industrial agglomeration of urban agglomerations is heterogeneous. In the BJHUA, the impact of HSR on the agglomeration of secondary and tertiary industries is not particularly significant. On the other hand, in the CPUA, HSR does not have a significant impact on the agglomeration of secondary industry. However, it does have a significant negative effect on the agglomeration of tertiary industry. In addition, further analysis reveals significant variations in the impact of HSR on the agglomeration of industries within urban agglomerations after excluding the central cities. It is important to note that the impact of HSR on regional industries can be complex and multifaceted. The findings enrich the theoretical understanding of the relationship between HSR and industrial agglomeration.

Predicting Changes in Population Exposure to Precipitation Extremes over Beijing–Tianjin–Hebei Urban Agglomeration with Regional Climate Model RegCM4 on a Convection-Permitting Scale

In this study, we have investigated changes in precipitation extremes and the population’s exposure to these extremes during 2091–2099 in China’s Beijing–Tianjin–Hebei (JJJ) region relative to the historical period of 1991–1999. First, the regional climate model RegCM4, with a hydrostatic dynamic core, was run for east Asia, including China, at a 12 km resolution for 1990–1999 and 2090–2099. This model is forced by global climate model (GCM) MPI-ESM1.2-HR under the middle shared socioeconomic pathways (SSP245). The first year was used as a model spinup. Then, the 12 km results were used to force RegCM4 with a non-hydrostatic dynamic core (RegcM4-NH) at a 3 km convection-permitting scale over the JJJ region during the historical and future periods. Future precipitation extremes were predicted to increase over the whole of China and its four subregions, while decreases were predicted over the JJJ region. This may partly be caused by lower increases in specific humidity over the JJJ region. The percentage contributions of the three components of total population exposure, i.e., changes in exposure due to changes in the population, precipitation extremes and the joint impact of the population and extremes, were then analyzed. Changes in the population and wet extremes were closely related to changes in the total exposure over the JJJ region. The population is the dominant factor that most impacts the total exposure to dry extremes. Finally, changes in future population exposure to precipitation extremes per degree of warming were quantified for the JJJ region.

Impacts of UHI on Heating and Cooling Loads in Residential Buildings in Cities of Different Sizes in Beijing–Tianjin–Hebei Region in China

The heating and cooling energy consumption levels of urban buildings account for a large and rapidly growing proportion of the total end-use energy consumption of society. The urban heat island (UHI) effect is an important factor influencing the spatiotemporal variations in the heating and cooling energy consumption levels of buildings. However, there is a lack of research on the impact of the UHI on the heating and cooling energy consumption of buildings in cities of different sizes in the Beijing–Tianjin–Hebei urban agglomeration, which is the most urbanized region in northern China. We selected rural reference stations using the remote sensing method, and applied an hourly data set from automatic weather stations, to examine the impact of the UHI on the typical residential building heating and cooling loads in three cities of varied sizes in the Beijing–Tianjin–Hebei urban agglomeration through building energy simulation. The main conclusions were as follows. As the UHI intensity (UHII) increased, the heating load difference between urban and rural areas decreased, while the cooling load difference between urban and rural areas increased in the cities. The average daily heating loads in the urban areas of Beijing, Tianjin, and Shijiazhuang were 8.14, 10.71, and 2.79% lower than those in their rural areas, respectively, while the average daily cooling loads in the urban areas were 6.88, 6.70, and 0.27% higher than those in their rural areas, respectively. Moreover, the absolute hourly load differences between urban and rural areas were significantly larger during the heating periods than during the cooling periods, with the former characterized by being strong at night and weak during the day. During the peak energy load period, the contribution of the UHI to the peak load of residential buildings varied between the cities. During the stable high-load period, from 18:00 to 07:00 the next day in the heating periods (from 18:00 to 05:00 the next day in the cooling periods), the hourly loads in the urban areas of Beijing, Tianjin, and Shijiazhuang were 3.15 (2.48), 3.88 (1.51), and 1.07% (1.09%) lower (higher) than those in their rural areas, respectively. Our analysis highlights the necessity to differentiate the energy supplies for the heating and cooling of urban buildings in different sized cities in the region.

A quantitative simulation and verification of the sustainable and low-carbon development efficiency curves for Beijing–Tianjin–Hebei urban agglomeration

The Beijing–Tianjin–Hebei urban agglomeration (BTHUA) has experienced ecological and environmental issues due to its rapid development and expansion, including air and water pollution. Examining inter-regional plans for sustainable and low-carbon sustainable development is crucial and practical for achieving ecological balance among regions and fostering the BTHUA’s commitment to collaborative innovation. This study applies the three-stage data envelopment analysis (DEA) model to 13 cities in the BTHUA between 2007 and 2020 to compute the sustainable and low-carbon development efficiency (SLE) and index, and then constructs a fundamental model of urban agglomeration growth. In this study, MATLAB software was used to predict the general evolution trend and development curve for the BTHUA’s low-carbon and green economy development. The results of the study indicate (1) the efficiency of sustainable and low-carbon development in the BTHUA has a wave-like ascending tendency, generating an overall development pattern that is centred on core cities and eventually descends toward the periphery. (2) In recent years, coordination between sustainable and low-carbon development indices and development levels within BTHUA has largely improved; however, a changing nonlinear relationship exists between the sustainable and low-carbon efficiency index and development levels. (3) The BTHUA’s sustainable and low-carbon development curve displays a tendency that is consistent with the function model’s anticipated evolution trend.

Potential grading refrigeration system? Based on urban agglomeration thermal environment analysis perspective

Urban heat stress is a critical issue impacting the sustainable development of urban agglomerations. Ecological land is an important factor in alleviating the heat environment stress of urban agglomerations. However, few scholars have taken a macro, interconnected perspective to consider the cooling effects of different ecological land. This study reveals that urban agglomerations have a potential hierarchical natural cooling system. Based on the heat environment assessment of the Beijing-Tianjin-Hebei (BTH) urban agglomeration and the Minimal cumulative resistance (MCR) model, we extracted the graded cooling patches and cooling corridors from 2005 to 2020. The research findings indicate that the heat environment intensity of the BTH urban agglomeration presents a spatial pattern of high intensity in the southeast and low intensity in the northwest, and the heat environment intensity of the urban agglomeration remains at a relatively high level. The number of graded patches and corridors shows a trend of first decreasing and then increasing, and the central and southern regions have relatively few corridors and patches, which require targeted optimization and improvement. Additionally, the overall length of corridors in the BTH urban agglomeration has increased, while the cooling effect of patches has decreased. The robustness and connectivity of the cooling system have both increased over 15 years, but further optimization and improvement are still needed to alleviate the heat environment intensity of urban agglomerations. This study quantifies the trends in urban agglomeration heat environment stress and the functional and topological properties of patches, corridors, and systems, providing valuable information for natural-based urban planning and management, as well as new solutions and research perspectives for alleviating heat environment stress in urban agglomerations.

Unraveling spatiotemporal patterns and multiple driving factors of surface ozone across China and its urban agglomerations management strategies

Since State Council launched the Action Plan for Air Pollution Prevention and Control in 2013, national concentration of fine particulate matter (PM2.5) has continued to decline in China, while surface ozone (O3) pollution shows an obvious rise. To identity hot regions and develop targeted policy, the spatiotemporal O3 variation and its population-weighted exposure features were analyzed in 337 cities across China, using autocorrelation analysis and grid exposure calculation. In the identified hot urban agglomerations, the correlation analysis and geographic weighted regression model (GWR) were used to study related meteorological factors and socioeconomic driving factors. O3 pollution and its human exposure were found to have significant spatial aggregation characteristics, showing a need for regional management policy. Beijing-Tianjin-Hebei Urban Agglomeration (BTH-UA), Central Plains Urban Agglomeration (CP-UA), and Yangtze River Delta Urban Agglomeration (YRD-UA) were identified as hot regions where O3 concentration exceeded 160 μg·m−3, exceedance rate was over 20% and population-weighted exposure risk was relatively high. Correlation analysis in the hot regions indicated high surface temperature, low relative humidity, and low wind speed were positive to O3 increase. Further, GWR results revealed that O3 in the majority of cities was positively related with population density (PD), the per capita GDP (Per_GDP), industrial soot emissions (ISE), industrial SO2 emissions (ISO2), and average annual concentration of inhaled fine particulate matter (PM10), and negatively related with total land area of administrative region (Administration) and area of green land (Green). From the regional driving factor difference, the targeted UA management policy was provided.

Carbon Emission Effects of Land Use in Chaobai River Region of Beijing–Tianjin–Hebei, China

Beijing–Tianjin–Hebei, the main economic area in northern China, has seen significant changes in its regional economic and physical landscape as a result of the coordinated development strategy. Assessing the link between land use and land cover (LULC) change and carbon emissions in the Chaobai River region, which represents the growth of the Beijing–Tianjin–Hebei urban agglomeration, is crucial to achieve coordinated low-carbon development in this area. This study uses statistics from statistical yearbooks of Chinese provinces and cities along with land use change data to analyze the relationship between land use changes and carbon emissions in the Chaobai River region from 2001 to 2017 using dynamic land use attitudes and land use transfer matrices, combined with carbon emission factors based on the IPCC inventory method and carbon emission models for energy consumption. In addition, this study makes use of the LMDI model and geographical detectors to identify and assess the factors that influence changes in land use carbon emissions and the driving forces behind the regional differentiation of land use changes. The results show that: (1) The Chaobai River region’s predominant land use classes during the past 17 years have been agricultural land and construction land. In addition to the decrease in cropland and the increase in urban land, the land use patterns of other land classes also changed to a certain extent. (2) Carbon emissions from land use showed an increasing trend, from 6.1 × 106 tons in 2001 to 1.1 × 107 tons in 2017. (3) Carbon emission intensity, economic development level, land use efficiency, and construction land scale have a certain regularity in the evolution of carbon emissions, and economic development level has become the most important driving factor controlling the growth of land use carbon emissions. (4) Driving factors in different periods have different degrees of influence on land use change, among which socio-economic factors such as population density and GDP have the strongest explanatory power. In addition, the interactions of each factor mainly present a double factor enhancement. In the future, the Chaobai River region should be based on the coordinated development strategy and take the “double carbon” target as its guiding principle to promote the innovation of the regional development system and further achieve the optimization of the regional land use patterns.

The Impact of Coordinated Development Policy on the Spatiotemporal Changes of Industrial Structure of Beijing–Tianjin–Hebei Urban Agglomeration

The coordinated development of Beijing–Tianjin–Hebei (BTH) is a major regional strategy in China that aims to alleviate Beijing’s non-capital functions and address the “big city disease”. Understanding the spatial distribution and changing trends of industrial development in BTH is critical for achieving BTH’s coordinated development goals. In particular, it is important to assess the effectiveness of Beijing’s non-capital functions at the industrial level. This study utilized the 2013 and 2018 economic census data and statistical methods such as spatial Gini coefficient, trend analysis, specialization index, and industry similarity. We first characterized the industrial development pattern by analyzing the spatiotemporal changes of the operating income and the number of legal entities in BTH urban agglomeration. Then, we identified the changes in the leading industries and industrial structure of different cities in BTH urban agglomeration from 2013 to 2018. The results indicate that the coordinated development policy has influenced the industrial structure of the BTH urban agglomeration, with an 85.53% increase in the number of legal entities and a 14.61% increase in operating income. Beijing’s non-capital functions have achieved initial results, mainly involving technology-intensive and knowledge-intensive tertiary industries such as information technology, finance, and scientific research. The division of industry and the development positioning of the three regions are gradually becoming clear. Our results show how the economic census data and spatial analysis can support significant advances in evaluating industrial and economic development patterns, and they can be used worldwide in the future.

Research on the Spatial Network Characteristics, Synergistic Emission Reduction Effects and Mechanisms of Carbon Emission in Beijing–Tianjin–Hebei Urban Agglomeration

With the in-depth advancement of the Beijing–Tianjin–Hebei coordinated development strategy, establishing a new mechanism for coordinated development in regional coordinated development is an important measure to realize the green, low-carbon and sustainable development of the Beijing–Tianjin–Hebei urban agglomeration. At present, there are significant differences in carbon emission intensity among different cities in the Beijing–Tianjin–Hebei urban agglomeration, which poses a significant obstacle to the synergistic development goals of the Beijing–Tianjin–Hebei region. Therefore, studying the carbon emissions of the Beijing–Tianjin–Hebei urban agglomeration is of great significance for achieving synergistic development in the Beijing–Tianjin–Hebei region and achieving China’s dual carbon goals. Based on the above practical background, this study focuses on the carbon emissions of the Beijing–Tianjin–Hebei urban agglomeration, using the gravity model, the social network analysis method, and the synergistic effects of carbon emission reductions model to analyze the general characteristics of the carbon emission spatial network, individual characteristics of the carbon emission spatial network, and synergistic effects of carbon emission reduction in the Beijing–Tianjin–Hebei urban agglomeration. The study found that the carbon emission spatial network of the Beijing–Tianjin–Hebei urban agglomeration presents a typical core–periphery structure. From the perspective of the general characteristics of the spatial network, the ranking structure of carbon emissions among cities in Beijing–Tianjin–Hebei is strict, and the network stability is good; from the perspective of individual characteristics of the spatial network, Beijing and Tianjin are the centers of the carbon emission spatial network of the Beijing–Tianjin–Hebei urban agglomeration, playing an important role of “betweenness” and “bridge”, while cities in Hebei Province are in a weak position in this regard. From the perspective of the measurement result of synergistic effects of carbon emission reduction, there is significant room for improvement in the synergistic effects of carbon emission reductions in the Beijing–Tianjin–Hebei urban agglomeration, and there are significant differences in the synergistic effects of carbon emission reduction among various cities. In general, at this stage, the core–periphery structure of the spatial network of carbon emissions in the Beijing–Tianjin–Hebei urban agglomeration is still solid, and the synergistic effects of carbon emission reduction between cities are weak. Establishing a sound synergistic mechanism of regional carbon emission reduction is the key to solving the carbon emission problem of the Beijing–Tianjin–Hebei urban agglomeration. Therefore, this study proposes countermeasures and suggestions to improve the synergistic mechanism of the reduction in carbon emissions in the Beijing–Tianjin–Hebei urban agglomeration from three levels—the formation layer, the implementation layer, and the guarantee layer—in order to promote synergistic emission reduction, ecological and environmental governance, and sustainable development of Beijing–Tianjin–Hebei urban agglomeration.