Beijing-Tianjin-Hebei Research Articles

Elucidating transport dynamics and regional division of PM2.5 and O3 in China using an advanced network model

Air pollution exhibits significant spatial spillover effects, complicating and challenging regional governance models. This study innovatively applied and optimized a statistics-based complex network method in atmospheric environmental field. The methodology was enhanced through improvements in edge weighting and threshold calculations, leading to the development of an advanced pollutant transport network model. This model integrates pollution, meteorological, and geographical data, thereby comprehensively revealing the dynamic characteristics of PM2.5 and O3 transport among various cities in China. Research findings indicated that, throughout the year, the O3 transport network surpassed the PM2.5 network in edge count, average degree, and average weighted degree, showcasing a higher network density, broader city connections, and greater transmission strength. Particularly during the warm period, these characteristics of the O3 network were more pronounced, showcasing significant transport potential. Furthermore, the model successfully identified key influential cities in different periods; it also provided detailed descriptions of the interprovincial spillover flux and pathways of PM2.5 and O3 across various time scales. It pinpointed major pollution spillover and receiving provinces, with primary spillover pathways concentrated in crucial areas such as the Beijing-Tianjin-Hebei (BTH) region and its surrounding areas, the Yangtze River Delta, and the Fen-Wei Plain. Building on this, the model divided the O3, PM2.5, and synergistic pollution transmission regions in China into 6, 7, and 8 zones, respectively, based on network weights and the Girvan Newman (GN) algorithm. Such division offers novel perspectives and strategies for regional joint prevention and control. The validity of the model was further corroborated by source analysis results from the WRF-CAMx model in the BTH area. Overall, this research provides valuable insights for local and regional atmospheric pollution control strategies. Additionally, it offers a robust analytical tool for research in the field of atmospheric pollution.

Spatiotemporal Evolution and Factors Influencing Regional Ecological Land in a Multidimensional Perspective: A Case Study of the Beijing–Tianjin–Hebei Region

A systematic analysis of the spatiotemporal evolution patterns and factors influencing ecological land (EL) can support the optimization of EL protection policies and ensure the stability of regional ecosystems. Based on remote sensing data, using the gravity center shift model, the landscape pattern index, and the equivalent factor method, the characteristics of EL evolution in the Beijing–Tianjin–Hebei (BTH) region from 1980 to 2020 were analyzed. A fixed-effects model was used to quantitatively explore the factors influencing EL evolution and heterogeneity analysis. The results are as follows: (1) The EL area exhibited a trend of initial decrease followed by a subsequent increase during the study period. The most significant area transfer occurred between cropland and EL, but, after the 21st century, the proportion of area transfer between construction land and EL noticeably increased. (2) The compactness and fragmentation of EL showed a certain spatiotemporal stability, but the spatial distribution of compactness and fragmentation hot and cold spots exhibited significant differences. The fragmentation hot spots mainly displayed a strip distribution, while those of compactness showed a clustered distribution. (3) Although the ecosystem service value in the BTH region demonstrated dynamic “M”-shaped changes, the distribution of hot and cold spots still exhibited spatial stability. Regulating services consistently occupied a higher proportion of the sub-service functions, while cultural services still needed further enhancement. (4) Factors influencing the evolution of areas and values demonstrated similarities. The landscape was significantly influenced by construction land, showing a non-linear “U”-shaped relationship with fragmentation. Different economic development gradients and altitudes exhibited differentiated characteristics in terms of their influencing factors. This study provides scientific support for dynamically and precisely adjusting governmental EL management policies, contributing to the sustainable development of regional socio-economics.

A Study on a Health Impact Assessment and Healthcare Cost Calculation of Beijing–Tianjin–Hebei Residents under PM2.5 and O3 Pollution

Excessive fine particulate matter (PM2.5) and ozone (O3) are invisible killers affecting our wellbeing and safety, which cause great harm to people’s health, cause serious healthcare and economic losses, and affect the sustainable development of the social economy. The effective evaluation of the impact of pollutants on the human body, the associated costs, and the reduction of regional compound air pollution is an important research direction. Taking Beijing–Tianjin–Hebei (BTH) as the research area, this study constructs a comprehensive model for measuring the healthcare costs of PM2.5 and O3 using the Environmental Benefits Mapping and Analysis Program (BenMAP) as its basis. First, this study establishes a health impact assessment model and calculates the number of people affected by PM2.5 and O3 exposure using the health impact function in the BTH region. Then, the willingness to pay (WTP) and cost of illness (COI) methods are used to estimate the healthcare costs inflicted by the two pollutants upon residents from 2018 to 2021. The calculation results show that the total healthcare costs caused by PM2.5 and O3 pollution in BTH accounted for 1%, 0.7%, 0.5%, and 0.3% of the regional GDP in 2018, 2019, 2020, and 2021, respectively. Based on the research results, to further reduce these high healthcare costs, we propose policy suggestions for PM2.5 and O3 control in the BTH region.

The Relationship between Farmland Abandonment and Urbanization Processes: A Case Study in Four Chinese Urban Agglomerations

Clarifying the relationship between urbanization and farmland abandonment in urban agglomerations (UAs) is crucial to guide the formulation of arable land management policies and strategies for sustainable urban development. Despite numerous studies confirming the correlation between farmland abandonment and certain urbanization factors, the exploration of the patterns and underlying mechanisms of farmland abandonment in China’s UAs remains worthy of systematic investigation. In this study, we conducted an analysis of the spatiotemporal trends in farmland abandonment and examined the key drivers of farmland abandonment in four representative Chinese UAs—Beijing–Tianjin–Hebei (BTH), Chengdu–Chongqing (CC), Pearl River Delta (PRD), and Yangtze River Delta (YRD). Our findings reveal that farmland abandonment has been intensified with increasing fragmentation and aggregation patches across these UAs. Abandonment experience was the main driver of continuous abandonment. Moreover, natural conditions persistently influenced farmland abandonment in the BTH, while land urbanization and economic urbanization were predominant drivers in the CC. The abandonment in the PRD was mainly driven by population urbanization, while the abandonment in the YRD was primarily driven by economic urbanization and land urbanization. The research findings provide data support and scientific explanation for land policy-making in these typical UAs under different development strategies.

Unravelling the non-linear response of ecosystem services to urban-rural transformation in the Beijing-Tianjin-Hebei region, China

In our transforming world, ecosystem services (ESs) and biodiversity are threatened by urban sprawl with rapid urban-rural transformation (URT), jeopardizing human well-being. Understanding the effect of URT on ESs is critical, particularly in areas those have undergone rapid urbanization. Taken the Beijing-Tianjin-Hebei (BTH) region of China as a typical case, this study measures the URT from the population-land-industry perspective and elucidates the influence of each index of URT on ESs at different stages. The URT process experienced a decline in population growth, an increase in land urbanization, and stability in non-agriculturalization rate. In particular, this study reveals an intricate and complex non-linear relationship between URT and ESs. URT negatively affects grain production (GP), habitat quality (HQ), soil retention (SR), and carbon sequestration (CS) and has a positive impact on water yield (WY). Furthermore, it was found that the response threshold of ESs to URTIs increased over time, indicating that ESs can maintain a certain degree of stability and resilience in the face of URT activities. However, ensuring the continued stability and resilience increasingly depends on the implementation of effective management and protection measures. This study advances our knowledge of the complex dynamics between URT and ESs in rapidly urbanizing areas and provides valuable insights for policymakers and planners to guide sustainable development and effective resource management.

Spatial and Temporal Distribution Characteristics of Ozone Concentration and Health Benefit Assessment in the Beijing-Tianjin-Hebei Region from 2015 to 2020

To evaluate the spatial and temporal distribution characteristics of ambient ozone (O3) in the Beijing-Tianjin-Hebei (BTH) Region, the land use regression (LUR) model and random forest (RF) model were used to simulate the ambient O3 concentration from 2015 to 2020. Meanwhile, all-cause, cardiovascular, and respiratory mortalities as well as economic losses attributed to O3 were also estimated. The results showed that upward trends with fluctuation were observed for ambient O3 concentration, mortalities, and economic losses attributable to O3 exposure in the BTH Region from 2015 to 2020. The areas with high O3 concentration and great changes were concentrated in the central and southwestern regions, whereas the concentration in the northern region was low, and the change degree was small. The spatial distribution of the mortalities was also consistent with the spatial distribution of O3 concentration. From 2015 to 2020, the economic losses regarding all-cause mortality and cardiovascular mortality increased in 13 cities of the BTH Region, whereas the economic losses of respiratory mortality decreased in 4 cities in the BTH Region. The results indicated that the priority areas for O3 control were not uniform. Specifically, Beijing, Tianjin, Hengshui, and Xingtai were vital areas for O3 pollution control in the BTH Region. Differentiated control measures should be adopted based on the characteristics of these target areas to decline O3 concentration and reduce health impacts and economic losses associated with O3 exposure.

Analyses and Simulations of PM2.5 Pollution Characteristics under the Influence of the New Year’s Day Effects in China

Regional haze often occurs after the New Year holiday. To explore the characteristics of PM2.5 pollutions under the influence of the New Year’s Day effect, this study analyzed the spatiotemporal changes relating to PM2.5 during and around the New Year’s Day holiday in China from 2015 to 2022, and used the Weather Research and Forecasting-Community Multiscale Air Quality (WRF-CMAQ) model to study the effects of human activities and meteorological factors on PM2.5 pollutions, as well as the differences in the contributions of different industries to PM2.5 pollutions. The results show that for the entire study period (i.e., before, during, and after the New Year’s Day holiday) from 2015 to 2022, the average concentrations of PM2.5 in China decreased by 41.9% overall. In 2019~2022, the New Year’s Day effect was significant, meaning that the average concentrations of PM2.5 increased by 18.9~46.8 μg/m3 from before to after the New Year’s Day holiday, with its peak occurring (64.3~74.9 μg/m3) after the holiday. In terms of spatial differences, the average concentrations of PM2.5 were higher in the Beijing–Tianjin–Hebei region, the Yangtze River Delta, and central China. Moreover, the Beijing–Tianjin–Hebei region and its surrounding areas, the Chengdu–Chongqing region, the Fenwei Plain, and the middle reaches of the Yangtze River region were greatly affected by the New Year’s Day effect. Human activities led to higher increases in PM2.5 in Henan, Hubei, Hebei, and Anhui on 3 and 4 January 2022. If the haze was accompanied by cloudy days or weak precipitation, the accumulation of surface water vapor and atmospheric aerosols further increased the possibility of heavy pollution. It was found that, for the entire study period, PM2.5 generated by residential sources contributed the vast majority (60~100 μg/m3) of PM2.5 concentrations, and that the main industry sources that caused changes in time distributions were industrial and transportation sources.

Trends of surface ozone based on hourly concentrations in the Beijing–Tianjin–Hebei region during 2017–2021

As a typical secondary air pollutant, surface ozone has been monitored routinely since 2013 in China. Most studies on the spatiotemporal variation of ozone have been centered around the daily maximum 8-h average, with little attention paid to the trends of hourly ozone, especially hourly ozone exceedances. Focusing on hourly ozone exceedances and peak values, the spatiotemporal trends of hourly ozone at 77 sites in 13 cities of the Beijing–Tianjin–Hebei (BTH) region during 2017–2021 were analyzed in this study. The number of hours with exceedances (NH200) in 2019 was nearly three times that of 2021. On a five-year average, the percentage of cumulative NH200 in June accounted for up to 40.5 % of all hourly exceedances. Cities in central Hebei Province had the highest cumulative annual NH200. June had the highest average hourly ozone exceeded multiples of 0.158. The top two cities with the highest average exceeded multiple were Tangshan (0.166) and Beijing (0.158). Tangshan and Xingtai ranked as the top two in terms of the mean of the 10 highest daily maximum ozone concentrations (MTDM), with 286.74 and 285.37 µg m−3, respectively. The gap between the MTDM and the daily maximum of hourly ozone averaged over all sites had narrowed to 97.88 µg m−3 in 2021, much lower than that in other years, which indicated that the stability and convergence of ozone pollution in BTH region had been enhanced in 2021 to some extent.摘要本文以京津冀地区为研究对象, 采用国际常用的评价指标, 在分析2017–2021年臭氧小时浓度变化趋势的基础上, 重点讨论了小时浓度超标情况及其峰值水平. 结果显示, 2019年臭氧小时浓度超标数约为2021年的3倍, 6月份小时浓度超标数占比高达40.5%, 河北中部城市小时浓度超标情况突出; 2017年和2019年臭氧小时浓度超标倍数在高位区间的占比相对最高, 唐山和邢台的小时浓度峰值水平相对较高. 本文可为后续进一步研究局地臭氧污染特征和异常污染事件提供基础参考.

Coking exhaust contributes to airborne particulate matter in the Beijing–Tianjin–Hebei region

Coking was regarded as a predominant source of air pollution. Despite the adoption of more environmentally friendly equipment, whether the coking enterprises in the Beijing–Tianjin–Hebei (BTH) region are still causing regional air pollution is worthy of study, which is essential for the control of coking enterprises in this area. To improve the prediction accuracy of large-scale air pollutant distribution, the air particle distribution in the BTH region was simulated via land use regression (LUR) combined with Bayesian maximum entropy (BME); then, the distribution was correlated with the exhaust gas emitted from coking enterprises. Results indicated that the R2 of the “LUR + BME” method reached 0.95, higher than 0.82 using LUR alone. The air quality distribution presented a pattern of “low in the northern mountains and high in the southern plains”, similar to the distribution of coking enterprises in BTH region. A significant correlation was found between exhaust emissions from coking enterprises and air quality in the BTH region, confirming the contribution of coking emissions to air pollution in this region, and the necessity to continue the strict control on coking enterprises in BTH area.

Spatiotemporal variation and prediction of NPP in Beijing-Tianjin-Hebei region by coupling PLUS and CASA models

Vegetation productivity is crucial for human production and livelihoods. Understanding net primary productivity (NPP) in historical contexts and predicting its future fluctuations is imperative for assessing the environmental sustainability of a region. However, relatively few researches have been conducted on predicting NPP, requiring further development and refinement of NPP prediction methods and models. This study introduces a novel approach that discretely couples the PLUS and CASA models for NPP prediction, and it validates the applicability of this approach in the research area. The objective of our study is to analyze the spatiotemporal patterns of NPP change in the Beijing-Tianjin-Hebei (BTH) region from 2001 to 2020, predict NPP under three different climate scenarios (SSP 1-2.6, SSP 2-4.5, SSP 5-8.5) in 2030, and identify an appropriate path for the future development of this region. The results indicate:(1) From 2001 to 2020, NPP in the research area has shown a gradual improvement trend and maintained a certain spatial distribution pattern in general. (2) The study discovered a correlation coefficient of 0.83 and an RMSE of 102.86 between predicted and actual NPP for 2020. This suggests that the method introduced in our study is suitable for predicting NPP in the research area. (3) NPP in the study area is predicted to decline in 2030 compared with 2020 under all three scenarios. Moreover, the SSP 1-2.6 scenario, representing a low-emission scenario, is suitable for the BTH region compared with other climate scenarios. This research sheds light on NPP variations in the BTH region over the past 20 years and the next 10 years, offering a scientific basis for relevant departments to formulate future policies.

Trends and Factors Influencing the Evolution of Spatial Patterns of Cropland toward Large-Scale Agricultural Production in China

Considering the essential expansion of agricultural production, current research primarily focuses on static factors, such as the distribution of fine-grained arable land, omitting an in-depth analysis of its developmental dynamics and key drivers. Addressing this knowledge gap is crucial for enhancing the scalability of agricultural production. This research utilizes landscape ecology techniques, correlation analysis, random forest algorithms, and structural equation modeling to explore spatial pattern trends of arable land in the Beijing–Tianjin–Hebei region. Its objective is to clarify how the expansion of agricultural production scale affects food production through changes in arable land patterns and to determine the impact of socio-economic factors on these configurations. The results show that: (1) the landscape pattern of arable land is transitioning to a more fragmented arrangement with complex contours, (2) grain yield per unit area correlates positively with the landscape pattern index in Beijing, negatively in Hebei, and exhibits no significant correlation in Tianjin, and (3) land ownership plays a crucial role in land fragmentation, alterations in land morphology, and influences other socio-economic variables. Analyzing the spatial pattern of arable land in conjunction with socio-economic factors is essential for developing holistic land management approaches, improving resource efficiency, minimizing external inputs, and mitigating food security challenges.

Using Zipf’s Law to Optimize Urban Spatial Layouts in an Urban Agglomeration Area

The study of the land use optimization of urban agglomerations is of great significance to the rational utilization of land resources and the sustainable development of urban agglomerations. Previous studies have simply regarded the urban agglomeration as a whole area or each city as an isolated individual, without considering the regional coordinated development of urban agglomerations in land use optimization objectives. This study proposed the first attempt to couple Zipf’s law and a multiobjective optimization model of land use in an urban agglomeration. Zipf’s law was applied to quantify the coordinated development of urban agglomeration and use it as one of the objectives of land use optimization. The genetic algorithm was adopted to establish the optimization model, and different future development scenarios were designed for comparison. The Beijing–Tianjin–Hebei (BTH) urban agglomeration was selected as the study area, and the proposed model obtained effective results within an acceptable time. The Zipf’s law–based optimization objective provided a more coordinated scale structure for urban growth and led the urban structure to develop toward the optimized state. There was a conflict, however, between the coordination and the other two objectives (compactness and suitability). Different stakeholders need to consider the trade-offs among these optimization objectives in urban planning. Finally, based on the optimization patterns of local areas, we suggest the following: Some large-scale cities (e.g., Beijing and Tianjin) should strictly control their new urban expansion or transfer part of their resources and population; Langfang and Xingtai should accelerate their development or take over part of the noncapital function of Beijing; and Zhangjiakou and Chengde should optimize their urban morphology and suitability.

Air quality and heath co-benefits of low carbon transition policies in electricity system: the case of Beijing–Tianjin–Hebei region

Low carbon transition strategies of power plants are crucial to meet China’s ‘Dual Carbon’ targets. While the Beijing–Tianjin–Hebei (BTH) region, the ‘capital economic circles’ of China, is suffering from serious air pollution, air quality co-benefits of low carbon transition policies in electricity system in BTH area remain unclear. Here, we estimate the impacts of low carbon transition policies, including one BAU, six single and five combined scenarios, in electricity system in BTH area on installed power capacity, CO2 emissions, air quality and human health through 2060 based on open source energy modeling system and extend response surface model ERSM models. Results show that the total installed capacity under single and combined scenarios (except RE and Tech single scenarios) fluctuates around the BAU level of 310.5 GW in 2060. While all single and combined scenarios would generally achieve ‘carbon peak’ in BTH electricity system before 2030, only S4 (combining technological progress, renewable energy development and CCUS) and S5 (in additional to S4, including gas-fired power generation instead of coal-fired power generation) scenarios have the potential to realize carbon neutrality by 2060. The magnitude of reductions in air pollutant emissions and improvement in air quality in BTH area from the BAU level in 2060 under combined scenarios, especially S4 and S5, generally exceed that of six single scenarios. Importantly, S5 in 2060 contributes to about 8528 avoided premature mortalities in BTH area, which are more effective than any other scenarios. The results suggest that S5 is a promising low carbon transition policy to achieve environmental improvement and produce health benefits.

The Effect of Technology Transfer Efficiency of Policies for Coordinated Development in the Beijing Tianjin Hebei Region: Evaluate by Using a Difference-in-differences Model

This paper uses the DID model to explore the impact of the implementation of the Beijing-Tianjin-Hebei coordinated development in 2014 on the efficiency of technology transfer in China. A comparative study was selected for the 6 years before and 5 years after the implementation of the policy. The technology transfer efficiency is calculated by the CCR and SBM of the DEA model. The scale of urban development and the financial capacity of the government are the control variables, which proves that the policy can improve the efficiency of regional technology transfer. The model passed parallel trend test and placebo test.

Efficiency measurement and inefficiency analysis of low-carbon logistics in the Beijing–Tianjin–Hebei region, China

Under the dual-carbon goals, enhancing the green development level of logistics industry and realizing its low-carbon transformation are important issues that need to be solved urgently. Amidst the continuous escalation in the total energy consumption of the national logistics industry, the Beijing-Tianjin-Hebei (BTH) region has exhibited a favorable descending trajectory in this respect. It is necessary to investigate the underlying reasons. Based on the panel data from 2012 to 2021, the DEA and Malmquist index are employed to analyze the low-carbon logistics efficiency of the BTH region from both static and dynamic perspectives. Furthermore, the inefficiency analysis is conducted to identify the deficiencies of low-carbon logistics industry in this region. Results show that (1) from the static perspective, the development of low-carbon logistics industry in the BTH region is relatively unbalanced. Compared to Tianjin and Hebei, Beijing's low-carbon logistics efficiency is significantly lower, becoming the focal area for attention; (2) from the dynamic perspective, technological progress is the main reason for the fluctuation of total factor productivity in the BTH region and a constraining factor for further improvements; (3) from the results of inefficiency analysis, the forthcoming emphasis on low-carbon logistics in Beijing should be on optimizing the number of logistics practitioners, transportation efficiency, and energy efficiency. Economic output and energy efficiency are relatively vulnerable aspects in Tianjin and Hebei, respectively, warranting due consideration. The research results of this paper have important practical implications for better developing low-carbon logistics in the BTH region and leveraging its leading role nationwide.

Research on Complex Network Models and Characteristics of Building Material Enterprises Based on Competitive Relationships

Based on complex network theory, this paper establishes a Boolean competitive relationship network model and a weighted competitive relationship network model for building material enterprises based on big data and performs empirical analyses with construction prefabricated component (PC) enterprises in the Beijing–Tianjin–Hebei region as research samples. Results show the competitive relationship network of PC enterprises in the Beijing–Tianjin–Hebei region is characterised by the development of a small-world network, and its cumulative degree and out-degree (in-degree) intensity distributions are single-scale networks with fast-decaying tails. The network has strong network clustering and assortativity, and it can truly reflect the competitive status and dynamics of the enterprises.

Investigating the multiscale associations between urban landscape patterns and PM1 pollution in China using a new combined framework

Urban landscape patterns serve as the spatial carriers of emission sources and influencing factors of air contaminants, which inevitably impact submicron particle (PM1) pollution. However, most studies ignore the spatial nonstationary and nonlinear effects of urban landscape patterns on the PM1 concentration. In this study, a novel framework was developed that integrates a multiscale geographically weighted regression (MGWR) model and Shapley Additive exPlanations (SHAP) machine learning method to explore the spatial heterogeneity effect, relative contribution and nonlinear impact of landscape patterns on the PM1 concentration at the national and urban agglomeration scales. The results indicated that urban landscape patterns were closely related to PM1 pollution and exhibited obvious spatial differences. The disorderly expansion of urban built-up areas and an irregular urban morphology could aggravate PM1 pollution, while urban fragmentation and connectivity could impose reducing effects. Dispersed urban landscape patterns could reduce the PM1 concentration in the Yangtze River Delta (YRD), Pearl River Delta (PRD), and Chengdu-Chongqing (CDCQ) regions, whereas compact and continuous landscape patterns positively affected PM1 pollution mitigation in the Beijing-Tianjin-Hebei (BTH) and Guanzhong Plain (GZH) urban agglomerations. The impact of urban landscape patterns on PM1 pollution was greater in urban agglomerations than elsewhere. The ENN_MN landscape index exhibited the highest feature importance and interpretability. The threshold effects between the urban shape indicators and PM1 concentration were more complex than those with the other landscape indices. This critical knowledge provides a scientific basis for further understanding the correlation mechanism between PM1 pollution and the landscape pattern, urban sustainable planning and air pollution control.

How to Realize Synergistic Emission Reduction in Future Urban Agglomerations: Spatial Planning Approaches to Reducing Carbon Emissions from Land Use: A Case Study of the Beijing–Tianjin–Hebei Region

Land use changes in rapidly urbanizing regions around the world constitute a principal anthropogenic element fueling the surge in carbon emissions. Here, land use patterns within the Beijing–Tianjin–Hebei (BTH) urban agglomeration under low-carbon development (LCD) scenarios were simulated. Additionally, social network analysis was employed to formulate carbon balance planning guidelines for various administrative regions. (1) In the ecological protection scenario, carbon emissions from land use were 643.42 × 104 tons lower compared to the natural development scenario. Counties with high ecological support coefficients accounted for 22%, making them better suited for predicting outcomes related to low-carbon-oriented land use. (2) The spatial connections of carbon emissions in BTH were closely related, forming the three main carbon emission spatial linkage areas. (3) A carbon balance zoning plan for the BTH in 2035 under the LCD scenario was formulated. Furthermore, key areas for the implementation of carbon peak and carbon neutrality projects were delineated, and targeted measures for carbon reduction and sink increase were proposed. This study provides a new perspective for implementing territorial spatial planning in Chinese urban agglomerations and can aid the government in formulating a reasonable low-carbon-oriented regional planning policy.

Exploring Spatial-Temporal Coupling and Its Driving Factors of Green and Low-Carbon Urban Land Use Efficiency and High-Quality Economic Development in China

Green and low-carbon use of urban land (GLUUL) and high-quality economic development (HED) are two closely linked and mutually reinforcing systems, and their coordinated development is of great theoretical and practical significance to the realization of green and sustainable urban development. Based on theoretical analysis, this paper used data from 2005 to 2020 to measure GLUUL efficiency and HED level and their coupling coordination degree (CCD) successively of 282 cities in China, and then analyzed in-depth the main factors affecting CCD and its spatial–temporal heterogeneity using the GTWR model. This study found that (1) GLUUL efficiency and HED levels are increasing with different trends, and the development is unbalanced. High-value cities in the two systems show a staggered distribution pattern. (2) The CCD of the two was dominated by primary and intermediate coordination types, and the overall became increasingly coordinated, with the “intermediate coordination—advanced development” type having the highest representation. (3) There is a gradual convergence of CCD spatial differences, showing an overall spatial distribution pattern that is “high in the northwest and southeast, low in the central area”. (4) The influence degree and direction of different factors on CCD are distinguishing. The positive impact of industrial structure upgrading (Isu) is obviously greater than other factors, which has the strongest effect on the industrial corridor along the Yangtze River and the Beijing–Tianjin–Hebei region. The findings can offer insightful recommendations for promoting sustainable development in China and similar developing countries and regions.

Estimation of All-Day Aerosol Optical Depth in the Beijing–Tianjin–Hebei Region Using Ground Air Quality Data

Estimation of All-Day Aerosol Optical Depth in the Beijing–Tianjin–Hebei Region Using Ground Air Quality Data