Hebei Region Research Articles

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.

Spatiotemporal differentiation and influencing factors of the degree of resilience coupling coordination in the Beijing–Tianjin–Hebei region, China

Urban agglomeration system resilience involves multiple subsystems that are interconnected and interactive, and exploring the relationships among subsystems can elucidate and increase urban agglomeration resilience. This study constructed a social-economic-built environment resilience model and proposed a social-economic-built environment resilience index system. Using the entropy weight method, the coupling coordination degree (CCD) model, GeoDetector, spatial autocorrelation analysis and the autoregressive integrated moving average model (ARIMA) model, the resilience development level of the Beijing‒Tianjin‒Hebei (BTH) region from 2000 to 2020 was evaluated, the main influencing factors for the spatiotemporal differentiation pattern were identified, and the development levels in 2030 and 2050 were predicted. The results show that (1) high-resilience and high-CCD regions are concentrated in the region of Beijing and Tianjin in the centre, and low-value regions are concentrated in the districts and counties in the west and south; (2) economic elements, especially the economic aggregate status of a region as a whole, are the main factors contributing to the differences in resilience levels between regions, but their influence gradually decreases; and (3) the gap between the CCDs of Beijing, Tianjin and cities in Hebei increased from 2000 to 2020, but this gap is expected to narrow by 2030 and 2050. On this basis, five categories of future development guidelines for cities in the BTH region are proposed.

How does China’s Winter Heating policy impact corporate sustainable development performance?

The China’s Winter Heating policy plays a crucial role in China’s regional environmental regulatory framework, particularly concerning the challenge of atmospheric pollution. This study investigates the impact of this policy on corporate sustainable development performance in the “2+26” cities of the Beijing, Tianjin and Hebei region (BTH2+26) at the micro-level. It employs a dual perspective focusing on emission reduction and efficiency enhancement (i.e., sustainable development), utilizing a DID model based on the policy shock from the first implementation of the China’s Winter Heating policy in 2017. The findings indicate that this policy not only effectively promotes pollution reduction among heavy-polluting companies, thereby enhancing their environmental performance, but also improves their financial performance. Robustness tests confirm the significance of these results. Mechanism analysis reveals that the policy’s facilitative effect on heavy-polluting companies is mediated through promoting green innovation and easing financing constraints, with the innovation investment being the main channel to promote the corporate environmental performance. Heterogeneity analysis shows differential impacts across company nature and industry attributes, highlighting the policy’s varied effects on environmental performance and financial performance across sectors. This research contributes empirical evidence and policy insights into enhancing sustainable development performance among heavy-polluting companies under the China’s Winter Heating policy framework.

Nitrogen cycling and associated grey water footprint in croplands under different irrigation practices

The pervasive application of nitrogen (N) fertilisers in agriculture poses a significant threat to freshwater ecosystems. The grey water footprint (GWF) is a measure of the volume of freshwater required to assimilate the relative water pollutants resulted from N leaching-runoff processes. The responses of N cycling and associated regional GWF to crop production under different irrigation methods have not been investigated. In this study, the N leaching-runoff rate, N₂O, and NH₃ emissions in croplands and the associated GWF per unit crop yield (UGWF) and annual total GWF (TGWF) of winter wheat-summer maize crop rotations across the Hebei, Shandong, and Henan Region (HSHR) of China were simulated and evaluated at the prefecture level over the 2004–2020 period. Four irrigation scenarios were modelled: furrow, sprinkler, surface drip, and subsurface drip irrigation. The results showed significant spatial and temporal heterogeneity in N leaching-runoff rates and emissions of N2O, and NH3. The N leaching-runoff rates for winter wheat and summer maize ranged from 3.9% to 10.9% and 15.0%–32.3%, respectively. With changing different irrigation methods, N leaching-runoff rates exhibited greater sensitivity than N₂O and NH₃ emissions; this sensitivity was more pronounced in winter wheat than in summer maize. Findings reveal that N pollution is influenced not only by irrigation methods but also by weather conditions, crop-specific rooting characteristics, sowing time, and soil texture. Winter wheat UGWF under the four irrigation methods mostly followed the order of furrow > sprinkler irrigation > surface drip irrigation > subsurface drip irrigation. Conversely, for summer maize, subsurface drip irrigation resulted in a higher UGWF compared to furrow irrigation, mainly due to increased water percolation thus higher N leaching-runoff rate in near-saturated soil with higher rainfall. This result reveals the potential conflict between water-saving irrigation and N pollution control. This analysis underscores the necessity of recognising the combined effects of agricultural management practices on water conservation and environmental safeguarding.

Spatial-temporal variations and Attribution analysis of SO2 concentration in Beijing-Tianjin-Hebei regions from 2013 to 2022

Based on the monitoring data of air pollutants in Beijing-Tianjin-Hebei regions from 2012 to 2022, the spatial-temporal distribution of SO2 concentration and main influencing factors were analyzed through multi-methods of Daniel trend test, KZ filtering and WRF/CMAQ model simulations. From 2013 to 2022, the annual averaged concentrations of SO2 in Beijing, Tianjin and Hebei regions were all in obvious downward trends, and all passed the Daniel trend test (α=0.01). The annual averaged concentration of SO2 in Beijing, Tianjin and Hebei regions decreased from 26.6~114.3μg·m-3 in 2013 to 3.0~11.0μg·m-3, with the decline rates fluctuating from 2.62~11.81μg/(m3·yr) while the decrease rate of SO2 in Beijing was the lowest. The average annual concentration of SO2 in Tangshan city decreased the most, reaching 92.3% and it changed to be the smallest, about 78.2% in Chengde City. The reduction of coal consumption in the Beijing-Tianjin-Hebei regions significantly reduced atmospheric SO2 emissions and directly reduced the regional average concentration of SO2. Meteorological factors were generally conducive to the diffusion of SO2 concentration during 2013~2022, and the meteorological factors contributed about 0.4~5.5% in 13 cities in Jingjinji area while the contribution of anthropogenic emission reduction was 94.5~99.6%. The inter-annual variation of meteorological factors in summer from 2013 to 2022 was not conducive to the diffusion of SO2 concentration on the whole, and the contribution changed to -52.6 ~ -1.0% in 13 cities. From June to August in summer under the easterly and southerly winds, the SO2 concentration in Beijing was basically twice that of the northerly and westerly winds. The contribution of southwest transport channel to SO2 concentration in Beijing was 33.5%, while it changed to 38.9% in the southeast transport channel. Therefore, strengthening the management and control of air pollutants in surrounding area of Beijing is helpful for Beijing to further reduce the levels of air pollutants and achieve targets of air quality improvement in the 14th Five-Year Plan period.

The Single-Scattering Albedo of Black Carbon Aerosols in China

Black carbon (BC) aerosols have attracted wide attention over the world due to their significant climate effects on local and global scales. BC extinction aerosol optical thickness (AOT), scattering AOT, and single scattering albedo (SSA) over China are systematically studied based on the MERRA-2 satellite reanalysis data from 1983 to 2022 in terms of the spatial, yearly, seasonal, and monthly variations. The extinction and scattering AOTs of BC show similar spatial distribution, with high values in eastern and southern China, generally as opposed to BC SSA. A decrease in BC extinction and scattering AOTs has been documented over the last decade. The mean BC extinction AOT, scattering AOT, and SSA over China are 0.0054, 0.0014, and 0.26, respectively. The BC SSA showed small variations during 1983–2022, although a high BC extinction AOT and scattering AOT have been seen in the last two decades. During different decades, the seasonal patterns of BC extinction and scattering AOTs may differ, whereas the BC SSA shows seasonal consistency. Significant monthly variations in the BC SSA are seen over four decades, which are in agreement with their seasonal patterns. The mean BC extinction AOTs are 0.037, 0.033, 0.023, and 0.0054, whereas the average BC scattering AOTs are 0.0088, 0.0082, 0.0060, and 0.0014 in the Pearl River Delta (PRD), Yangtze River Delta (YRD), Beijing–Tianjin–Hebei (BTH) region, and Tarim Basin (TB), respectively. It is interesting to see that BC SSA values in the TB region are generally higher than those over the PRD, YRD and BTH areas, whereas the reverse is true for BC extinction and scattering AOTs. This study provides references for further research on black carbon aerosols and air pollution in China.

ACEIC: a comprehensive anthropogenic chlorine emission inventory for China

Abstract. Chlorine species play a crucial role as precursors to Cl radicals, which can significantly impact the atmospheric oxidation capacity and influence the levels of trace gases related to climate and air quality. Several studies have established a chlorine emission inventory in China in recent years, but the emission remains uncertain and requires further investigation. The Anthropogenic Chlorine Emission Inventory for China (ACEIC) was the first chlorine emission inventory for China based on local data developed in our previous study, which only includes the emissions from coal combustion and waste incineration. In this study, we updated this inventory to include data for a more recent year (2019) and expanded the range of species considered (HCl, fine particulate Cl−, Cl2, and hypochlorous acid (HOCl)) and the number of anthropogenic sources (41 specific sources). Compared with previous studies, this updated inventory considered more anthropogenic sources, used more localized emission factors, and adopted more refined estimation methods. The total emissions of HCl, fine particulate Cl−, Cl2, and HOCl in mainland China for the year 2019 were estimated to be 361 (−18 % to 27 %), 174 (−27 % to 59 %), 18 (−10 % to 15 %), and 79 (−12 % to 18 %) Gg, respectively. To facilitate analysis, we aggregated the chlorine emissions from various sources into five economic sectors: power, industry, residential, agriculture, and biomass burning. HCl emissions were primarily derived from the industry (43 %), biomass burning (38 %), and residential (13 %) sectors. The biomass burning and industry sectors accounted for 74 % and 19 % of the fine particulate Cl− emissions, respectively. Residential and industry sectors contributed 61 % and 29 % of the total Cl2 emissions. HOCl emissions were predominantly from the residential sector, constituting 90 % of the total emissions. Notably, the usage of chlorine-containing disinfectants was identified as the most significant source of Cl2 and HOCl emissions in the residential sector. Geographically, regions with high HCl and fine particulate Cl− emissions were found in the North China Plain, northeastern China, central China, and the Yangtze River Delta, whereas the Pearl River Delta, Yangtze River Delta, and Beijing–Tianjin–Hebei regions exhibited elevated levels of Cl2 and HOCl emissions. Regarding monthly variation, emissions of HCl and fine particulate Cl− were higher during early spring (February to April) and winter (December to January) due to intensified agricultural activities, while Cl2 and HOCl emissions were higher in the summer months due to increased demand for water disinfection. We incorporated this emission inventory into the chemical transport model and found the simulated concentrations of chlorine species agreed reasonably well with the observations, which suggested the relatively faithful estimations of their emissions. This updated inventory contributes to a better understanding of anthropogenic sources of chlorine species and can aid in the formulation of emission control strategies to mitigate secondary pollution in China.

Spatiotemporal Evolution of Land Use Structure and Function in Rapid Urbanization: The Case of the Beijing–Tianjin–Hebei Region

Spatiotemporal Evolution of Land Use Structure and Function in Rapid Urbanization: The Case of the Beijing–Tianjin–Hebei Region

Assessing comprehensive anthropogenic impacts at a regional scale using ecological integrity

Various anthropogenic disturbances, such as urbanization, agricultural production, and infrastructure development, are contributing to global and regional ecosystem degradation. However, comprehensive quantitative assessment methods for evaluating regional-scale anthropogenic impacts remain insufficient. This study introduces a comprehensive assessment framework based on ecological integrity (EI) derived from the landscape condition model (LCM), combined with commonality analysis and other quantitative methods. This framework was applied to the Beijing–Tianjin–Hebei (BTH) region in North China to assess the spatio-temporal variations and spatial aggregation of EI, identify the major drivers of EI degradation, and determine the formation mechanisms of resultant EI patterns. The anthropogenic impacts, as measured by EI, exhibit significant spatial and aggregation heterogeneity across functional zones. This heterogeneity, along with the observed temporal variations (2000–2022), is attributed to diverse anthropogenic disturbances within intricate biophysical and socioeconomic contexts. This framework can indirectly improve the R2 of multiple linear regression models related to EI by identifying grassland and cropland as suppressor variables to enhance the predictive capacity of other variables. Consequently, this framework enables decision-makers to accurately identify major drivers of EI degradation, such as road construction and urbanization, and understand the combined effects of major drivers and suppressor variables on the resultant EI patterns. Lastly, within specific policy contexts, this framework offers several management implications for mitigating anthropogenic threats to EI, including effectively regulating urbanization and restoring forest landscapes. This framework provides fundamental support for anthropogenic impact assessment and management strategy design at a regional scale.

Inter-annual changes in transboundary air quality from KORUS-AQ 2016 to SIJAQ 2022 campaign periods and assessment of emission reduction strategies in Northeast Asia

Northeast Asia suffers from high concentrations of particulate matter (PM), prompting nations to actively implement emission reduction policies. This study evaluated the recent inter-annual changes in the chemical transformation and transboundary transport of PM over Northeast Asia, based on both ground and aircraft measurements, as well as WRF-Chem simulations, during two comprehensive campaigns: the Korea–United States Air Quality (KORUS-AQ) campaign in 2016 and the Satellite Integrated Joint Monitoring of Air Quality (SIJAQ) campaign in 2022, both conducted around the Korean Peninsula. Ground measurements in 2022 revealed significant reductions in air pollutants compared to 2016 levels. In the Beijing–Tianjin–Hebei region, PM2.5 and SO2 concentrations decreased by 47.2% and 73.9%, respectively, attributable to successful SOx and NOx emission reduction strategies. Similar trends were observed in downwind areas, including Seoul, where PM2.5 and SO2 levels declined by 30.0% and 41.4%, respectively. WRF-Chem model results indicated substantial decreases in sulfates, nitrates, and their precursors in both surface and upper atmosphere in 2022 compared to 2016. Moreover, model-calculated gas-to-particle conversion ratios, which peaked in the Yellow Sea in 2016, decreased by 15% in 2022 and shifted slightly eastward to the western Korean Peninsula. This shift suggests a potential decline in secondary PM formation processed in the Yellow Sea, coinciding with reduced long-range transport of gaseous pollutants. A comparison of model sensitivity experiments, accounting for both bottom-up emission changes and meteorological variations, revealed that while weather and climate factors such as precipitation and pressure patterns between 2016 and 2022 contributed to the overall decrease in PM concentrations, the primary driver was the reduction in emissions during this period. This study highlighted that the main driver of the substantial improvement in air quality over East Asia was the implementation of emission reduction policies targeting PM and its precursors in the main source regions in China.

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.

Spatial–Temporal Development Trends and Influencing Factors of Government Environmental Information Disclosure: Empirical Evidence Based on China’s Provincial Panel Data

Government environmental information disclosure (GEID) plays an important role in promoting the digital transformation of environmental governance, leading the concept of sustainable development, enhancing public oversight capacity, and promoting democratic decision-making governance. Using provincial panel data from China spanning from 2009 to 2021, we conducted spatial data exploratory analysis and used the dynamic spatial panel model to investigate the spatial–temporal development trends and influencing factors of GEID. The results show that (1) GEID in China exhibits significant spatial agglomeration characteristics, with an “H-H” (High-High aggregation type) agglomeration characteristic observed in three national strategic development regions: Yangtze River Delta, southeast coastal areas, and Beijing–Tianjin–Hebei region. (2) The spillover effect from the southeast coastal provinces gradually radiates to the northwest, resulting in an overall westward movement of GEID. (3) GEID exhibits a significant path-dependency feature in the temporal dimension and a “peer effect” in the spatial dimension. (4) Population size has the greatest impact on GEID. Population size, public participation, and the industrial and transportation sectors positively influence GEID improvement at the local level. However, they generate negative spillover effects to neighbouring provinces. Environmental status and the size of the Real Estate sector have no significant effect. Therefore, China should strengthen regional cooperation, narrow regional disparities, cultivate new quality productive forces, establish a government-led proactive disclosure mechanism under public supervision, and improve the level of GEID at the national level.

Prediction and spatial–temporal changes of soil organic matter in the Huanghuaihai Plain by combining legacy and recent data

Soil organic matter (SOM) is critical for soil fertility, crop growth, and plays an important role in the global carbon cycle and climate change. Therefore, spatial prediction of SOM is important to rational soil resource utilization, agricultural production, and ecological environment management. However, large-area SOM mapping research heavily relies on legacy soil data, and large-scale recent SOM mapping may not be possible or have lower accuracy due to limited or less recent data availability. In this study, we aimed to improve SOM prediction and mapping accuracy by combining legacy data with limited recent data. Three models, namely, partial least squares regression (PLSR), random forest (RF), and one-dimensional convolutional neural network (1D-CNN), were applied and compared. The results showed that combining legacy and recent data effectively improved SOM prediction accuracy compared to using only recent data. Among the three modeling methods, 1D-CNN exhibited superior performance, with an averaged determination coefficient of the prediction (R2) of 0.58, a root mean square error (RMSE) of 4.56 g/kg, and a ratio of performance to interquartile distance (RPIQ) of 2.05. The predicted SOM content for both legacy (1980 s) and recent (2010 s) periods showed similar spatial distribution patterns throughout the Huanghuaihai Plain. Generally, there was a noticeable trend of increasing SOM content from northwest to southeast, with higher values observed in Jiangsu and lower values concentrated in Henan, Hebei, and Shandong regions within the study area. Over time, SOM contents in the Huanghuaihai Plain showed an increasing trend, with an average increase of 5.90 g/kg from legacy to recent period. This study provides a promising approach for improving SOM prediction and mapping accuracy at large scales, particularly when recent data availability is limited.

Integrated health risk assessment of ozone and nitrogen dioxide pollution during the cold and warm seasons in the Beijing–Tianjin–Hebei region

Integrated health risk assessment of ozone and nitrogen dioxide pollution during the cold and warm seasons in the Beijing–Tianjin–Hebei region

Research on the synergy of the water–energy–food composite system in the Beijing–Tianjin–Hebei region

ABSTRACT Water, energy, and food are interdependent and mutually restrictive, and changes in any one resource may have an impact on the others. In order to promote the sustainable utilization of resources, it is important to study the synergy of the water–energy–food composite system (WEFCS). Taking the Beijing–Tianjin–Hebei region as an example, this study adopts the composite system synergy degree model to quantitatively measure the synergy degree of WEFCS from 2011 to 2021, then constructs an evolutionary model to identify the interactions between the subsystems, and finally analyzes the interactions dynamically by using the panel vector autoregression model. The results show that the WEFCS synergy level in the Beijing–Tianjin–Hebei region is generally basic and shows low stability during the study period. Regional subsystems are more dependent than collaborative or competitive. Moreover, the regional water subsystem has a short-term positive impact on the energy subsystem, the energy subsystem has a short-term negative impact on the food subsystem, and the food subsystem has a delayed and complex influence on the water and energy subsystems, with the water subsystem being particularly affected. This study provides a decision-making basis for policymakers to optimize resource allocation.

Analysis of Global and Key PM2.5 Dynamic Mode Decomposition Based on the Koopman Method

Understanding the spatiotemporal dynamics of atmospheric PM2.5 concentration is highly challenging due to its evolution processes have complex and nonlinear patterns. Traditional mode decomposition methods struggle to accurately capture the mode features of PM2.5 concentrations. In this study, we utilized the global linearization capabilities of the Koopman method to analyze the hourly and daily spatiotemporal processes of PM2.5 concentration in the Beijing–Tianjin–Hebei (BTH) region from 2019 to 2021. This approach decomposes the data into the superposition of different spatial modes, revealing their hierarchical spatiotemporal structure and reconstructing the dynamic processes. The results show that PM2.5 concentrations exhibit high-frequency cycles of 12 and 24 h, as well as low-frequency cycles of 124 and 353 days, while also revealing spatiotemporal modes of growth, recession, and oscillation. The superposition of these modes enables the reconstruction of spatiotemporal dynamics with a mean absolute percentage error (MAPE) of only 0.6%. Unlike empirical mode decomposition (EMD), Koopman mode decomposition (KMD) method avoids mode aliasing and provides a clearer identification of global and key modes compared to wavelet analysis. These findings underscore the effectiveness of KMD method in analyzing and reconstructing the spatiotemporal dynamics of PM2.5 concentration, offering new insights into the understanding and reconstruction of other complex spatiotemporal phenomena.

Analysis of Synergistic Drivers of CO2 and NOX Emissions from Thermal Power Generating Units in Beijing–Tianjin–Hebei Region, 2010–2020

Synergistic control of the emissions of air pollutants and CO2 is critical to the dual challenges of air quality improvement and climate change in China. Based on the emission inventories of thermal power units in Beijing, Tianjin, and Hebei, this study analyzes the CO2 and NOX emission characteristics of these units, and identifies and quantifies the synergistic drivers affecting these emission trends. The inventory data show that, between 2010 and 2020, NOX emissions were reduced by 86.1%, while CO2 emissions were reduced by only 29.8%. Although significant progress has been made in reducing NOX emissions through measures such as end-of-pipe treatment, controlling CO2 emissions remains a difficult task. The index decomposition analysis reveals that economic growth is the main driver of CO2 and NOX emission growth, energy intensity reduction is the main driver of CO2 emission reduction, and end-of-pipe treatment is the main driver of NOX emission reduction. Currently, coal occupies about 87% of the energy consumption of thermal power units in the Beijing–Tianjin–Hebei region, and remains the main type of energy for synergistic emissions, and the potential for emission reduction in the energy structure remains huge. For NOX emissions, it is expected that 90% of the reduction potential can be achieved through energy restructuring and end-of-pipe treatment. In conclusion, this high-precision unit-by-unit emission study confirms the effectiveness of the control policy for thermal power units in the region and provides some scientific reference for future policy formulation.

Is More Always Better? Government Attention and Environmental Governance Efficiency: Empirical Evidence from China

In recent years, the thorough implementation of China’s green development concept has compelled local governments to devote more attention to environmental issues. This study aimed to verify whether increased government environmental attention (GEA) can sustainably ensure the implementation of environmental governance, particularly air pollution control. Using government work reports (GWRs) from local governments, this study employed machine learning methods to identify and quantify the attitudes of government officials as expressed in policy texts. A weighted dictionary method was used to quantify GEA from 2011 to 2016. The results of spatial econometric models indicated that air pollution exhibited positive spatial clustering effects across different regions, with the Yangtze River Delta and the Beijing–Tianjin–Hebei region being classified as high–high areas, while the western regions were classified as low–low areas. Baseline regression results showed that increased GEA can improve the effectiveness of pollution control, but excessive attention leads to a decline in governance efficiency. Overall, this study helps explain the unsustainability of campaign-style environmental governance and provides guidance for local governments on the rational allocation of attention when addressing environmental issues.

The Influence Effect of Foreign Direct Investment on the Resource Mismatch in The Beijing-Tianjin-Hebei Region

In order to explore whether the continuous influx of foreign direct investment helps to improve the resource mismatch in the Beijing-Tianjin-Hebei region, this paper makes an empirical analysis of the data of the Beijing-Tianjin-Hebei region from 2003 to 2019, and finds that the inflow of FDI has a significant positive impact on the regional resource mismatch, especially the resource mismatch of capital factors. On this basis, a dynamic panel model was added to further verify the positive effect of FDI on improved capital mismatch. The study also found that the impact of foreign direct investment on resource mismatch in Beijing-Tianjin-Hebei region has very obvious regional heterogeneity, which shows the improvement effect of foreign direct investment on capital mismatch in Hebei region, but no obvious improvement effect on Beijing and Tianjin. Therefore, in order to further improve the Beijing-Tianjin-Hebei region resources mismatch, accelerate Hebei economic development to narrow the gap with the Beijing and Tianjin, increase and effective use of foreign direct investment is a favorable for economic high quality development, suggested that the government in the FDI inflow at the same time, strengthen the regional coordinated development, promote resource balanced allocation, improve the regional resource coordination and allocation, in order to realize the sustainable development of the Beijing-Tianjin-Hebei region as a whole.

Patterns of Species Richness and Its Endemism of Beetles in the Beijing–Tianjin–Hebei Region of China

The Beijing–Tianjin–Hebei region lies between the Mongolia-Xinjiang Zoogeographic Region and the Central China Zoogeographic Region in China, harboring relatively rich biodiversity. This study specifically examined the species diversity, richness and endemic areas of beetles in this area. By analyzing 5965 detailed distribution records of 2047 beetle species in the Beijing–Tianjin–Hebei region, the species richness maps were created with a grid size of 0.5°. Additionally, two methods, parsimony analysis of endemism (PAE) and endemicity analysis (EA) were applied to detect areas of endemism (AOEs) with different grid sizes (0.1°, 0.25° and 0.5°), resulting in the identification of three AOEs in the southern mountainous areas of the Taihang Mountains, Yanshan Mountains, and Xiaowutai Mountains. It also verified that AOEs are consistent with the hypothesis that endemic areas are predominantly located in mountain ranges, as proposed by previous related studies. These findings highlight the importance of complex topography and stable climate in shaping AOEs and conserving biodiversity.