Agglomerations In China Research Articles

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.

Industrial Dry Heat Island and Dispersion of Air Pollutants Induced by Large Coal-Fired Activities.

Notable anthropogenic heat sources such as coal-fired plants can alter the atmospheric boundary layer structure and the pollutant dispersion, thereby affecting the local environment and microclimate. Herein, in situ measurements inside a coal-fired steel plant were performed by multiple advanced lidars from 21 May to 21 June of 2021 in Yuncheng, Shanxi Province, China. Comparing with an adjacent meteorological site, we found a prominent nighttime dry heat island overhead of the factory, which was 3-10 °C hotter and 30%-60% drier than the surrounding fields. Large-eddy simulations constrained by the measured thermal contrast suggested that the heat-island-induced circulation could upward transport factory-discharged pollutants and horizontally spread them below the residual layer top, forming a mushroom-shaped cloud. The shape, size, and pollutant loading of the cloud were highly determined by thermodynamic variables such as aerodynamic wind and anthropogenic heat flux. Furthermore, these retained residual-layer pollutants can be convected downward to the ground after sunrise through the fumigation effect, causing the peaking phenomena aboveground. These peaks were statistically evidenced to be common in major urban agglomerations in China. The study provides a new insight regarding the origins of residual-layer pollutants and highlights the needs for programming representations of coal-fired heat emissions in mesoscale air-quality models.

Spatial Expansion Characteristics and Nonlinear Relationships of Driving Factors in Urban Agglomerations: A Case Study of the Yangtze River Delta Urban Agglomeration in China

Urban agglomerations are increasingly becoming the primary regional units in global competition, characterized by the rapid expansion of impervious surface areas, which negatively impacts both society and the environment. This study quantifies the spatiotemporal expansion of these surfaces in the Yangtze River Delta urban agglomeration and explores its driving factors using a Geographically Weighted Random Forest model. The results demonstrate a transition from “point expansion” to “infill development”, while also revealing a gradual southward shift in the developmental focus of the Yangtze River Delta urban agglomeration. Although expansion intensity has decreased, spatial clustering has intensified. Based on the expansion patterns of impervious surface areas, we propose a novel regional classification method, dividing the Yangtze River Delta urban agglomeration into three zones: “A-Development Decline Zone”, “B-Development Core Zone”, and “C-Development Ascendance Zone”. Socio-economic factors are the primary drivers of this expansion, followed by science and education, and then the ecological environment, while physical geography factors have the least impact. The study reveals differentiated regional development characteristics and further refines the sub-regions within the urban agglomeration, providing a new perspective for future regional coordinated development policies.

Exploration of multiple enhancing pathways of digital economy development of city clusters using fuzzy-set qualitative comparative analysis

As a crucial catalyst for worldwide economic expansion, the digital economy (DE) has garnered widespread attention and has been regarded as crucial for the promotion of economic growth and innovation. The development of the DE brings both significant opportunities and challenges to city clusters. It enhances urban competitiveness, improves residents’ quality of life, and strengthens urban governance, which is of great significance for the development of city clusters. In this context, although previous studies comprehensively analyzed the advancement of the DE of certain cities and revealed their strategies, challenges, and successful experiences in the development of their DE, research on the determinants and enhancing pathways of the digital economy development of city clusters (DEDCC) is scant. As the core node of DE development, city clusters, as well as their potential and influence, cannot be ignored. Therefore, this study investigates the Shandong Peninsula Urban Agglomeration in China, constructs a theoretical analysis framework for the DEDCC, and utilizes fsQCA (Fuzzy-set Qualitative Comparative Analysis) to examine the complex causal processes of the DEDCC. The fsQCA method is utilized from a configurational perspective to explore the complex driving mechanisms of DEDCC. The objective is to investigate the pathways for enhancing DEDCC and provide insights for the DE development of other city clusters. Results reveal the following: (1) the reciprocal collaboration between market factors and fundamental factors should be examined to accelerate the high-quality DEDCC. (2) technological innovation (TI) capacity is a crucial determinant of the high-quality DEDCC. (3) the coordinated development of the market, the foundation, and TI should be prioritized to achieve the high-quality DEDCC.

Remote Sensing Fine Estimation Model of PM2.5 Concentration Based on Improved Long Short-Term Memory Network: A Case Study on Beijing–Tianjin–Hebei Urban Agglomeration in China

The accurate prediction of PM2.5 concentration across extensive temporal and spatial scales is essential for air pollution control and safeguarding public health. To address the challenges of the uneven coverage and limited number of traditional PM2.5 ground monitoring networks, the low inversion accuracy of PM2.5 concentration, and the incomplete understanding of its spatiotemporal dynamics, this study proposes a refined PM2.5 concentration estimation model, Bi-LSTM-SA, integrating multi-source remote sensing data. First, utilizing multi-source remote sensing data, such as MODIS aerosol optical depth (AOD) products, meteorological data, and PM2.5 monitoring sites, AERONET AOD was used to validate the accuracy of the MODIS AOD data. Variables including temperature (TEMP), relative humidity (RH), surface pressure (SP), wind speed (WS), and total precipitation (PRE) were selected, followed by the application of the variance inflation factor (VIF) and Pearson’s correlation coefficient (R) for variable screening. Second, to effectively capture temporal dependencies and emphasize key features, an improved Long Short-Term Memory Network (LSTM) model, Bi-LSTM-SA, was constructed by combining a bidirectional LSTM (Bi-LSTM) model with a self-adaptive attention mechanism (SA). This model was evaluated through ablation and comparative experiments using three cross-validation methods: sample-based, temporal, and spatial. The effectiveness of this method was demonstrated on Beijing–Tianjin–Hebei urban agglomeration, achieving a coefficient of determination (R2) of 0.89, root mean squared error (RMSE) of 12.76 μg/m3, and mean absolute error (MAE) of 8.27 μg/m3. Finally, this model was applied to predict PM2.5 concentration on Beijing–Tianjin–Hebei urban agglomeration in 2023, revealing the characteristics of its spatiotemporal evolution. Additionally, the results indicated that this model performs exceptionally well in hourly PM2.5 concentration forecasting and can be used for PM2.5 concentration hourly prediction tasks. This study provides technical support for the large-scale, accurate remote sensing inversion of PM2.5 concentration and offers fundamental insights for regional atmospheric environmental protection.

Carbon Footprint Accounting and Verification of Seven Major Urban Agglomerations in China Based on Dynamic Emission Factor Model

Amidst the prevailing trends in environmental conservation and the imperatives of energy conservation and emission reduction, the precision in assessing and forecasting carbon emissions has acquired heightened significance. The conventional emission factors, typically derived from historical data and empirical knowledge, often remain unchanged and fail to swiftly account for the reductions in emissions that are a consequence of technological advancements and green innovations. (1) This paper establishes a dynamic emission factor model, then uses city data and provincial data to verify the model, and compares the research results of other relevant researchers. The research results show that this method not only considers the different characteristics of energy types, but also considers regional differences and industry characteristics, making the emission factor more suitable for the actual situation. The results show that the method takes into account not only the different characteristics of energy types, but also regional differences and industry characteristics, making the emission factor more suitable for the actual situation. (2) This paper systematically compares the diverse methods for calculating the carbon footprints of Chinese provinces and cities. It encompasses a spectrum of methods, including carbon footprint accounting based on emission factors, accounting based on dynamically adjusted emission factors, and accounting from the perspective of carbon sinks. Each of these methods possesses its own set of applicable scenarios and inherent limitations. The emission factor method is apt for basic carbon emission accounting, while the adjusted emission factor method is tailored for scenarios where the evolution of technology and shifts in energy paradigms are pivotal. Concurrently, the carbon sink accounting framework is optimally suited for the evaluation of the carbon footprint within the realm of natural ecosystems.

The fate of atenolol in wastewater treatment plants of representative densely urban agglomerations in China.

Atenolol (ATL) that can decrease heart rate and reduce renin release is extensively used in the treatment of hypertension, angina, and other diseases. ATL's popularity has therefore drawn attention to its environmental behavior and potential impacts. Wastewater treatment plants (WWTPs) are the main collection point for ATL entering the water environment, highlighting the necessity of studying its fate in the environment. In this study, five WWTPs with different processes in China's representative densely urban agglomerations (Pearl River Delta [PRD] region) were selected as certain representative sampling sites to investigate the fate of ATL in the WWTPs over four seasons. Results showed that ATL concentration in the influent of these WWTPs was 146.5-918.6ng/L and the corresponding concentration in the effluent was 43.0-534.1ng/L, achieving a certain degree of removal. The seasonal ATL removal performance varied greatly among these WWTPs, Liede, Tai Po, and Sha Tin WWTPs showed better removal stability. Meanwhile, the average ATL removal rates in Tai Po (83.36%) and Sha Tin (81.67%) were higher than those in the Futian (71.24%), Liede (55.44%), and Stonecutters Island WWTPs (44.96%). The primary treatment capacity of Futian WWTP was better than that of Tai Po and Sha Tin WWTPs in removing ATL, while the performance of secondary treatment was opposite. Moreover, Zahn-Wellens test demonstrated that ATL could be almost completely degraded after 30 days and some protonated molecules (e.g., m/z 145 and m/z 190) metabolites were formed, indicating that degradation may play a role in ATL removal in WWTPs.

Research on Multi-Scenario Simulation of Urban Expansion for Beijing–Tianjin–Hebei Region Considering Multilevel Urban Flows

With the development of urban agglomerations in China, the study of the interactions between cities has become a popular and difficult issue. Exploring the interactions between cities can help decision-makers optimize regional resource allocation and improve regional spatial patterns. Combining the urban flow model and the patch-generating land use simulation (PLUS) model, this study simulates and analyzes the process of urban expansion in the Beijing–Tianjin–Hebei (BTH) region, and investigates the impact of urban hierarchical structure differences on urban expansion. In this study, the role and influence of inter-city economy flow, transportation flow, population flow, and information flow on the development of urban agglomerations are comprehensively considered, and a multilevel urban interaction model is constructed based on a hierarchical generalized linear model (HGLM). Based on the national BTH cooperation and development strategy, a multi-scenario simulation study of urban expansion is carried out using the HGLM-PLUS model. The results indicate the following: (1) compared to the traditional PLUS model, the coupled HGLM-PLUS model, which considers multilevel urban flows, improved the overall accuracy by 0.047, the Kappa coefficient by 0.207, and the figure of merit (FoM) index by 0.051; (2) under different simulation scenarios, the development trend under the cooperation and development policy in the BTH region is more stable, demonstrating a relatively smooth urbanization expansion trend; and (3) under the BTH cooperation and development background, the total area of construction land in the BTH region is expected to be maintained at around 1,164,500 km2 by 2040. The spatial expansion pattern will present a networked expansion with the core driving development, axes and belts connecting, and clusters breaking through.

County-Level Spatiotemporal Dynamics and Driving Mechanisms of Carbon Emissions in the Pearl River Delta Urban Agglomeration, China

Encouraging cities to take the lead in achieving carbon peak and carbon neutrality holds significant global implications for addressing climate change. However, existing studies primarily focus on the urban scale, lacking more comprehensive county-level analyses, which hampers the effective implementation of differentiated carbon mitigation policies. Therefore, this study focused on the Pearl River Delta urban agglomeration in China, adopting nighttime light data and socio-economic spatial data to estimate carbon emissions at the county level. Furthermore, trend analysis, spatial autocorrelation analysis, and Geodetector were adopted to elucidate the spatiotemporal patterns and influencing factors of county-level carbon emissions. Carbon emissions were predominantly concentrated in the counties on the eastern bank of the Pearl River Estuary. Since 2010, there has been a deceleration in the growth rate of carbon emissions in the region around the Pearl River Estuary, with some counties exhibiting declining trends. Throughout the study period, construction land expansion consistently emerged as a predominant factor driving carbon emission growth. Additionally, foreign direct investment, urbanization, and fixed asset investment each significantly contributed to the increased carbon emissions during different development periods.

Characteristics and Source Analysis of Ozone Pollution in Tianjin from 2013 to 2022

This study has analyzed ozone pollution in Tianjin from 2013 to 2022, focusing on the relationships between ozone distribution, meteorological conditions, and precursor substances. A method for identifying high-value areas of ozone precursors using the Ozone Sensitivity Factor (FNR) has been introduced. Results show that the average ozone concentration in Tianjin has been 100.608 µg/m3, with an annual growth rate of 2.84 µg·m⁻3·yr⁻¹. Tianjin has ranked among the top provinces and urban agglomerations in China for both ozone concentration and growth rate. Ozone levels have peaked in summer, followed by spring, autumn, and winter, while the growth rate has been highest in spring. This indicates that ozone pollution extends from summer into spring and autumn. An analysis of six ozone pollution events reveals significant regional transmission impacts from northern Hebei and Inner Mongolia, contributing over 30%, with additional significant contributions from southern and southwestern Hebei and western Shandong. In terms of controlling ozone precursors, high-HCHO-value areas have been identified. The correlation between areas of high HCHO values and ground-level ozone concentrations was 0.56339 during the ozone season and 0.2214 during the non-ozone season, both of which improved identification accuracy to varying degrees, suggesting that targeting precursor emissions in these areas could enhance pollution mitigation efforts.

Regional carbon inequality and its impact in China: A new perspective from urban agglomerations

The principal urban agglomerations in China function as critical hubs for both economic advancement and the mitigation of carbon emissions. Given the disparate levels of development across various urban agglomerations, trade between these agglomerations may result in inequality between carbon transfers and value added transfers. Assessing carbon inequality among major urban agglomerations in China can help identify the identification of primary trade relationships and industrial sectors of carbon inequality, thereby fostering the coordinated development of urban agglomerations. This study constructs a carbon inequality index based on an environmentally extended input-output model. This study conducts a thorough evaluation of carbon inequality among various urban agglomerations and analyzes the industry characteristics of embodied carbon net transfers and value added net transfers in typical urban agglomerations. The research findings indicate that: The carbon inequality index for underdeveloped urban agglomerations, exemplified by the Central Plains urban agglomeration, is generally greater than 1. The carbon inequality index for developed urban agglomerations, exemplified by the Yangtze River Delta urban agglomeration, is generally less than 1 due to their higher levels of economic development. An analysis of the industry characteristics of carbon emissions net transfer and value added net transfer in typical urban agglomerations reveals that heavy industry is the main contributing sector to the net inflow of embodied carbon and net outflow of value added in the Central Plains urban agglomeration. The main contributing sector to the net outflow of embodied carbon and net inflow of value added in the Yangtze River Delta urban agglomeration is the service industry. This study ultimately proposes more refined recommendations aimed at fostering coordinated development among regions in China, establishing a win-win situation for economic growth and carbon reduction.

Characteristics and health risks assessment of PM2.5-bound heavy metals during winter in urban areas of northern China: A case study in Kaifeng

The health impact of heavy metals in atmospheric PM2.5 has garnered increasingly widespread attention. We have collected PM2.5 samples in a typical city (Kaifeng) within the Central Plains Urban Agglomeration in China during winter and measured the mass concentration of PM2.5-bound heavy metals. The pollution of As and Cr in the urban atmosphere requires significant attention. As PM2.5 concentrations increased, the enrichment factors (EFs) of Mn, Cu, Zn, As, and Pb also rose, suggesting a growing contribution from anthropogenic emissions. The analysis showed that the hazard quotient (HQ) for the non-heating and heating periods (HQ < 1) did not result in a cumulative non-carcinogenic health risk to humans. Regarding carcinogenic effects, As and Cr exhibit significant carcinogenic impacts on both children and adults (ELCR>1 × 10−6), indicating that the carcinogenic risks posed by As and Cr under PM2.5 exposure in Kaifeng could not be overlooked. It was found that industrial and biomass combustion are the primary sources of carcinogenic risk in Kaifeng city. From the non-heating to the heating period, the industrial carcinogenic risk increased from 37.12 % to 43.39 %, while the contribution of biomass burning remained at 25 %. This result was strongly correlated with the high proportions of heavy metal elements such as As, Mn, Pb, and Ni from the metal refinery industry. The results of this study revealed the equally important source of heavy metals, compared to coal combustion in North China Plain. In addition to residential coal combustion, industrial emissions are a major source of PM2.5-bound heavy metals in Kaifeng, contributing significantly to overall air pollution and providing a useful reference to mitigating human health risks in the area.

Evolution and simulation optimization of rural settlements in urban-rural integration areas from a multi-gradient perspective: A case study of the Lan-Bai urban agglomeration in China

With the rapid growth of global urbanization, rural land has been continuously occupied, which has brought about profound changes in human-land relationship. As a rural regional system in the frontier area of urbanization, the rural settlements in the urban-rural integration area (URIA) are in a profound transformation due to multiple disturbances such as internal natural factors and social and economic factors brought about by external urbanization. Based on the internal and external system factors of rural settlements, this paper constructs an analytical framework to explore the spatial and temporal evolution pattern of URIA rural settlements from 2000 to 2020, and then summarizes its evolution model, and predicts the situation of rural settlements in different scenarios in 2030. Finally, the optimal control strategy is proposed. The results show that: (1) From 2000 to 2020, affected by the gradient of natural environment, the scale of rural settlements in URIA shows a trend of decreasing in low-altitude areas near rivers and increasing in high-altitude areas far from rivers. Under the socio-economic gradient, there is a phenomenon that the scale of rural settlements in the inner edge of URIA with better socio-economic environment decreases and the scale of settlements in the outer area of URIA with poor socio-economic environment increases. (2) Based on different environmental factors, in the internal natural environment, it shows the evolution mode of “terrain containment” and “water source guidance”, while in the socio-economic environment, it shows the evolution mode of “urban sprawl encroaches rural”, “population decrease and land increase”. (3) The simulation results of rural settlements in 2030 show that the scale of rural settlements in the periphery of URIA continues to increase under the natural development (ND) scenario, while the scale of rural settlements in the inner edge decreases and the distribution is scattered. Under the urban-rural integration development (URD) scenario, rural settlements are mainly concentrated in the core area of URIA, and the area of rural settlements turning into urban land has dropped significantly. (4) Based on different gradients, the settlements are divided into three types: centralized development type, restricted development type and migration remediation type. Patch reconstruction is carried out by gravity model, which can theoretically save land and vacate 327 hm2 of construction land index. Compared with the extensive development model of the ND scenario, the URD scenario has important theoretical significance in limiting the blind expansion of the city and realizing the integration of urban and rural development in Lanbai.

Measurement and Analysis of Carbon Emission Efficiency in the Three Urban Agglomerations of China

China aims to reduce its carbon emissions to achieve carbon peaking and neutrality. Measuring the carbon emission efficiency of three urban agglomerations in China, exploring their spatiotemporal characteristics, and investigating the main influencing factors are crucial for achieving regional sustainable development and dual carbon goals. Using the super-slack-based measurement (super-SBM) model, we calculated the carbon emission efficiency of the Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) urban agglomerations from 2011 to 2021 and explored the spatiotemporal non-equilibrium characteristics of carbon emission efficiency and its influencing factors. The results indicated that: (1) Overall, the carbon emission efficiency showed an N-type trend, with the PRD having the highest average efficiency. Regional differences between the YRD and BTH regions gradually increased. (2) The efficiency hotspots shifted from the PRD to the YRD, whereas the cold spots were mainly concentrated in the BTH region. The variation in the standard deviation ellipse radius of carbon emission efficiency in the urban agglomerations was clear, and the spatial disequilibrium was significant. (3) Economic level and opening up had positive impacts on carbon emission efficiency, whereas energy intensity and industrial structure had negative impacts. The effects of population size, government intervention, and technological level varied among the regions.

Evaluation of the coordination-difference-driven sustainability of 12 urban agglomerations in China based on the dynamic probability weighting method

The sustainable development of urban agglomerations represents a significant driving force in national and global development. This study establishes an indicator system comprising factors associated with the economy, society, and environment, in accordance with the Triple Bottom Line, to assess the sustainability of 12 urban agglomerations in China. A novel framework is proposed, including a dynamic probability weighting method based on sufficient stochastic simulations and a coordination-difference-driven aggregation approach that considers the coordination degree and differences between evaluated objects. The evaluation revealed significant regional disparities in urban agglomeration sustainability from 2012 to 2021. The eastern region's Yangtze River Delta, Pearl River Delta, Beijing–Tianjin–Hebei region, and Shandong Peninsula exhibit above-average sustainability performance. Conversely, the western region's Guangzhong, Guangxi Beibu Gulf, Chengyu, and Ningxia Yellow River regions exhibit below-average performance. Moreover, the growth rate of sustainability values for the 12 urban agglomerations followed a downward trajectory. Furthermore, the environmental dimension is the primary driver of sustainable development in urban agglomerations, while the economic dimension represents the main obstacle. These findings offer policymakers a scientific and practical framework to guide sustainability-related decisions.

Quantitative simulation and validation of energy carbon emission efficiency changes in Chinese urban agglomerations

In the context of the global energy dilemma, enhancing energy efficiency has inevitably become a pivotal strategy for mitigating energy waste and promoting sustainable development. Urban agglomerations, as growth poles leading high-quality regional economic development, play a significant role in this process. Within these urban agglomerations, the aggregation of various factors and the substitution of energy resources substantially influence the energy carbon emission efficiency (ECEE) of surrounding cities during their development and maturation phases. This study initially employs a three-stage Data Envelopment Analysis (DEA) to quantitatively analyze the ECEE values of 19 urban agglomerations from 2006 to 2020. It then establishes a methodology for calculating the joint intensity (JI) and joint threshold (JT) of ECEE within these urban agglomerations. Finally, a validation process is executed using MATLAB simulation and verification methodology to fit and authenticate the evolutionary patterns of ECEE in Chinese urban agglomerations. The primary objective of this research is to model the evolution of ECEE, thereby exploring the sustainable development pathways for urban agglomerations in China. The research findings indicate that: (1) The results from the three-stage DEA demonstrate that, after removing the influences of external environmental factors and random errors, the comprehensive technical efficiency (TE) of energy carbon emissions for the 19 urban agglomerations in China experienced a wave-like increase, rising from 0.410 to 0.506 between 2006 and 2020. (2) The calculation results of the constructed JI model indicate that the JT values of ECEE for national-level, regional-level, and local-level urban agglomerations are 1006.92, 226.60, and 160.14, respectively. The national-level urban agglomerations have significantly higher JT values and JI than the other two levels of urban agglomerations. (3) The results of the Matlab simulation verification show that 16 urban agglomerations fit well with the wave-like ascending evolutionary pattern. However, four urban agglomerations exhibit an opposite fitting effect due to the influence of their pillar industries or the small number of cities within these accumulations, making them unrepresentative. Consequently, the evolutionary curve of ECEE in Chinese urban agglomerations generally exhibits a wave-like upward trend over time.

Site selection and effects of background towers on urban CO2 estimates: A case study from central downtown Zhengzhou in China

With China's proposed carbon reduction goals, many carbon monitoring pilot city projects have been launched, involving greenhouse gas (GHG) inverse estimate analysis based on GHG observations. For the evaluation of emissions estimates in a targeted urban area, the contributions of extra-urban fluxes on urban GHG observations must be excluded, especially for core cities within urban agglomerations, which face more severe emission interference from adjacent cities. In this study, we quantified the impact of external emissions on urban carbon dioxide (CO2) mole fraction observations across different seasons in the central downtown area of Zhengzhou, a core city of the Central Plains Urban Agglomeration in China. Results showed that 60% of the CO2 enhancement from the 500-km square area including the city originated outside the core urban area in autumn and winter, predominantly originating from far-field sources (>50 km) in the northeast, west, and northwest of Zhengzhou. To design an optimal monitoring network that accurately accounts for CO2 mole fractions entering the urban domain of interest, three different selection methods (distance, meteorological trajectory, and multiple regression) were used to select background station locations, and the resulting background values were evaluated through the application of observing system simulation experiments, including synthetic flux inverse estimate. Results indicated that the background stations selected by meteorological trajectories more effectively captured CO2 variability, introducing the smallest errors to inverse estimate flux (−8%). This study provides a valuable reference for designing background monitoring stations in dense urban agglomerations, thereby improving the accuracy of high-resolution urban GHG emission inverse estimates.

Land Space and High-Speed Transportation Coordinated Development Evaluation in the Beijing–Tianjin–Hebei Urban Agglomeration of China

There is a complex interaction between national land space and transportation. China is accelerating its strategy of building a strong transportation country and territorial spatial planning, which requires evaluating and strengthening the coordination relationship between transport and land space to promote development. Taking the Beijing–Tianjin–Hebei urban agglomeration in China as an example, this study analyze the high-speed transportation construction and land space development process, evaluates the coupling and coordination degree between high-speed transportation and land space, as well as the “development-protection” situation in the years 2000, 2010, and 2020 by means of the coupling and coordination model, spatial autocorrelation analysis, and GIS analysis. The result shows: (1) high-speed transportation construction and territorial space development have been active for a long time, and most of the cities have maintained a high level of increase in these two indicators. In terms of protection indicators, Tangshan and other coastal cities have been lagging behind for a long time without any improvement trend. (2) The coupling and coordination level between high-speed transportation and development is high, with slight weakening over time, with an average annual decrease of less than 0.09%. The change in the coupling coordination level with protection shows the wave dynamics of ‘low-middle-low’. (3) In the future, high-speed transportation will have a significant impact on land space in the country for a long time. In this regard, it is suggested to: (1) promote the construction of high-speed transportation for network, systematic, and serial development; (2) accelerate the extension of the high-speed transportation advantages of the existing growth poles of Beijing and Tianjin; and (3) develop Shijiazhuang and Langfang as the new growth poles. This study will promote the integration of transportation and urban planning, which will contribute to the development of high-quality synergistic urban agglomerations around the world.

Analysis of disequilibrium and driving factors of carbon emission efficiency: Evidence from five major urban agglomerations in China

Urban agglomerations (UAs) are not only highly integrated economic communities but also communities of shared destiny with integrated environmental protection and ecological construction. Although low-carbon development within UAs has garnered significant attention, a lack of comprehensive analysis remains regarding the imbalances and driving factors influencing carbon emission efficiency (CEE) in these regions. This study investigates the CEE of China's five national UAs from 2006 to 2020 by incorporating the meta-frontier framework into the super slack-based model. The global Malmquist index analyzes dynamic trends in CEE by decomposing the total factor productivity of carbon emissions to examine the catch-up, innovation, and technological leadership effects. In addition, this paper employs the Theil index to explore the disequilibrium of CEE among the five UAs. Outcomes reveal that, first, CEE has regional heterogeneity, with the Pearl River Delta UA (PRDUA) having the highest CEE and the Beijing–Tianjin–Hebei UA (BTHUA) having the lowest CEE. The gap between BTHUA's CEE and that of other UAs is widening. Second, the overall CEE holds improvement room, with its disequilibrium resulting from uneven city growth within the UA. Third, the innovation effect is the key to improving CEE. Fourth, PRDUA serves as the technical leader among all UAs, while the two UAs in the inland area possess the potential for technical leadership. Finally, drawing from the empirical results, this paper provides specific suggestions for advancing industrial transformation, boosting innovation capabilities, and minimizing internal imbalances within UAs. The goal is to steer UAs in China toward low-carbon development.

How Do Changes in Ecosystem Services Multifunctionality Influence Human Wellbeing? Evidence From the Yangtze River Delta Urban Agglomeration in China

ABSTRACTA proclaimed goal of landscape management is to improve the multifunctionality of ecosystem services (ESs) to sustain higher levels of human wellbeing (HWB). Although the enhancement of ES multifunctionality is often considered to lead to better HWB, empirical evidence directly supporting this claim remains scarce. This study investigates the relationship between ES multifunctionality and HWB in rapidly urbanizing regions, using the Yangtze River Delta Urban Agglomeration in China as a case study. We quantified ES multifunctionality with the Gini‐Simpson Diversity Index and identified ES multifunctional types using the bundle approach. We also calculated both the HWB mean score and HWB evenness score with the improved radar chart method, examining their relationships with ES multifunctionality through Spearman correlation analysis and the Kruskal–Wallis rank‐sum test. Results showed that (1) ES multifunctionality exhibited varied relationships with HWB indicators, but overall showed significant negative associations with both HWB mean score and HWB evenness score. (2) Each HWB indicator significantly differed across the detected ES bundles, but none of the bundles exhibited the highest values of all HWB indicators. (3) HWB mean score and HWB evenness score were generally higher in the peri‐urban bundle while lower in the agriculture and forest bundles. Our findings suggest that peri‐urban landscapes can provide relatively higher and more balanced levels of HWB in urban agglomerations. This also implies that a “land‐sharing” urban development model, which balances natural and built environments, may be more beneficial for enhancing ES multifunctionality and HWB compared with a “land‐sparing” model, where natural and built areas are separated.