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High spatiotemporal resolution data on near-surface ozone concentration distribution is of great significance for monitoring and controlling atmospheric ozone pollution and improving the living environment. Using TROPOMI-L3 NO2, HCHO products, and ERA5-land high-resolution data as estimation variables, an XGBoost-LME model was constructed to estimate the near-surface ozone concentration in the Beijing-Tianjin-Hebei Region. The results showed that： ① Through correlation analysis, surface 2 m temperature （T2M）, 2 m dewpoint temperature （D2M）, surface solar radiation downwards （SSRD）, tropospheric formaldehyde （HCHO）, and tropospheric nitrogen dioxide （NO2） were important factors affecting the near-surface ozone concentration in the Beijing-Tianjin-Hebei Region. Among them, T2M, SSRD, and D2M had strong correlations, with correlation coefficients of 0.82, 0.75, and 0.71, respectively. ② Compared with that of other models, the XGBoost-LME model had the best performance in terms of various indicators. The ten-fold cross-validation evaluation indicators R2, MAE, and RMSE were 0.951, 9.27 µg·m-3, and 13.49 µg·m-3, respectively. At the same time, the model performed well at different time scales. ③ In terms of time, there was a significant seasonal difference in near-surface ozone concentration in the Beijing-Tianjin-Hebei Region in 2019, with the concentration changing in the order of summer > spring > autumn > winter. The monthly average ozone concentration in the region showed an inverted "V" trend, with a slight increase in September. The highest value occurred in July, whereas the lowest value occurred in December. In terms of spatial distribution, the near-surface ozone concentrations in the Beijing-Tianjin-Hebei Region during the months of February and March were generally at the same levels. In January, November, and December, there was a relatively insignificant trend of higher concentrations in the north and lower concentrations in the south. For the remaining months, the spatial distribution of near-surface ozone concentrations in this area predominantly exhibited a pattern of higher concentrations in the south and lower concentrations in the north. High-value areas were predominantly found in the plain regions of the southern part with lower altitudes, dense population, and higher industrial emissions； low-value areas, on the other hand, were primarily located in mountainous areas of the northern part with higher altitudes, sparse population, higher vegetation coverage, and lower industrial emissions.

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Although machine learning methods have enabled considerable progress in air quality assessment, challenges persist regarding data privacy, cross-regional data processing, and model generalization. To address these issues, we introduce an advanced federated Bayesian network (FBN) approach. By integrating federated learning, adaptive optimization algorithms, and homomorphic encryption technologies, we substantially enhanced the efficiency and security of cross-regional air quality data processing. The novelty of this research lies in the improvements implemented in federated learning for air quality data analysis, particularly in distributed model training optimization and data consistency. Through the integration of adaptive structural modification strategies and simulated annealing immune optimization algorithms, we markedly enhanced the structural learning accuracy of the Bayesian network, resulting in a 20% improvement in prediction accuracy. Moreover, employing homomorphic encryption ensured data transmission security and confidentiality. In our Beijing-Tianjin-Hebei case study, our method demonstrated a 15% improvement in air quality classification accuracy compared to conventional methods and exhibited superior interpretability in analyzing environmental factor interactions. We quantified complex air pollution patterns across regions and found that a 30% fluctuation in the air quality index correlated with NO2 concentrations. We also observed a moderate positive correlation between specific pollutant indicators in Hebei Province and Tianjin and changes in air quality. Additionally, the FBN exhibited better operational efficiency and data confidentiality than other machine learning models in handling large-scale and multisource environmental data. Our FBN approach presents a novel perspective for environmental monitoring and assessment, vital for understanding complex air pollution patterns and formulating future ecological protection policies.

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The Beijing-Tianjin-Hebei (BTH) is one of the key areas with PM2.5 air pollution in China. Driven by the PM2.5 target accessibility of the Interim Target-1 (IT-1) by World Health Organization (WHO) and China's carbon neutrality, this study explored and quantified the contribution of climate change and anthropogenic emission to future PM2.5 in the region. The experiments considered future climate change scenarios RCP8.5, RCP4.5, and RCP2.6 with the baseline (Base) and reduced emission (EIT1) inventories in 2030, and RCP4.5 climate scenario with 3 emission inventories in 2050, the additional strong control emission scenario called Best-Health-Effect (BHE). Under various climate scenarios, the future air quality research modelling system projected annual PM2.5 concentrations nearing 35 µg/m3 in 2030. However, considering only the effect of emission reduction, the annual PM2.5 concentrations under EIT1 emission scenario is about 35% less than under Base scenario in different key years. The future PM2.5 concentrations are highly related to anthropogenic emission from human activities, while climate change by 2030 or 2050 has little impact on future air quality over the BTH region. The BHE emission reduction is significantly required for China to meet the new PM2.5 guideline value of WHO in the future.

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Exploring the spatiotemporal variations of vegetation net primary productivity (NPP) and analyzing the relationships between NPP and its influencing factors are vital for ecological protection in the Beijing-Tianjin-Hebei (BTH) region. In this study, we employed the CASA model in conjunction with spatiotemporal analysis techniques to estimate and analyze the spatiotemporal variations of NPP in BTH and different ecological function sub-regions over the past two decades. Subsequently, we established three scenarios (actual, climate-driven and land cover-driven) to assess the influencing factors and quantify their relative contributions. The results indicated that the overall NPP in BTH exhibited a discernible upward trend from 2000 to 2020, with a growth rate of 3.83 gC·m-2a-1. Furthermore, all six sub-regions exhibited an increase. The Bashang Plateau Ecological Protection Zone (BP) exhibited the highest growth rate (5.03 gC·m-2a-1), while the Low Plains Ecological Restoration Zone (LP) exhibited the lowest (2.07 gC·m-2a-1). Geographically, the stability of NPP exhibited a spatial pattern of gradual increase from west to east. Climate and land cover changes collectively increased NPP by 0.04 TgC·a-1 and 0.07 TgC·a-1, respectively, in the BTH region. Climate factors were found to have the greatest influence on NPP variations, contributing 40.49% across the BTH region. This influence exhibited a decreasing trend from northwest to southeast, with precipitation identified as the most influential climatic factor compared to temperature and solar radiation. Land cover change has profound effects on ecosystems, which is an important factor on NPP. From 2000 to 2020, 15.45% area of the BTH region underwent land cover type change, resulting in a total increase in NPP of 1.33 TgC. The conversion of grass into forest brought about the 0.89 TgC increase in NPP, which is the largest of all change types. In the area where land cover had undergone change, the land cover factor has been found to be the dominant factor influencing variations in NPP, with an average contribution of 49.37%. In contrast, in the south-central area where there has been no change in land cover, the residual factor has been identified as the most influential factor influencing variations in NPP. Our study highlights the important role of land cover change in influencing NPP variations in BTH. It also offers a novel approach to elucidating the influences of diverse factors on NPP, which is crucial for the scientific assessment of vegetation productivity and carbon sequestration capacity.

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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.

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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.

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Increasing socioecological systems (SESs) sustainability requires establishing a reasonable cross-regional social and ecological interaction. In this study, we examine how cross-regional ecological and social interactions affect synergistic effects. Using InVEST and correlation analysis with data from 2010 through 2020, we assessed ESs (i.e., water retention-WR, nutrient retention-NR, and carbon storage-CS) in the Beijing-Tianjin-Hebei (BTH) region. A small watershed, a river network, and settlement development capacity are used to delineate ecological and social interactions units. Based on a Bayesian network model that considers population, economy, and spatial agglomeration patterns between social units, we assessed the potential for achieving a synergistic improvement of ESs and the driving forces behind them. The results show that ESs in the BTH region compete, only a small percentage (6.38%) shows synergetic improvement across CS, WR, and NR. It is beneficial for upstream watersheds to retain water and nutrients, but to maintain carbon storage they may sacrifice water retention. Upstream areas with less development and higher vegetation density have better ecosystem integrity of up- and down-stream watersheds, and can be enhanced with minimal human impact, as social interactions and settlement spatial structures influence ES synergies. There is a higher risk for ecological issues in downstream areas, but greater awareness and collaboration can lead to better ES synergies.

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Notably, clear spatial differences occur in the distribution of air pollution among cities in the Beijing-Tianjin-Hebei (BTH) Region. Clarifying the concentration distribution of PM2.5 and O3 at different time scales is helpful to formulate scientific and effective pollution prevention and control measures. Here, the concentrations of PM2.5 and O3 were decomposed using a seasonal-trend decomposition procedure based on the loess (STL) method; their long-term, seasonal, and short-term components were obtained; and their temporal and spatial distribution characteristics were studied. The results showed that the decrease in PM2.5 concentration in the BTH Region from 2017 to 2021 was higher than that of O3. There was a positive correlation between PM2.5 and O3 concentrations in spring and summer and a negative correlation in autumn and winter. The short-term component and seasonal component had the greatest contribution to PM2.5 and O3 concentrations, respectively. There were two principal components in the seasonal and short-term components of PM2.5 and the long-term and short-term components of O3, corresponding to the central and southern part of Hebei Province and the northern part of the BTH Region. Sub-regional distribution of PM2.5 and O3 in the BTH Region at different time scales were found. Compared with that in the original series, the long-term component could better reflect the evolution trend of PM2.5 and O3 concentrations, and the standard deviation (SD) of the seasonal component and short-term component could be used to measure the fluctuation in PM2.5 and O3 concentrations in various cities. The SD of the seasonal and short-term components of the PM2.5 concentration in every city in front of Taihang Mountain was higher, and the SD of the short-term component of the O3 concentration in Tangshan was the highest.

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The Beijing-Tianjin-Hebei region is not only an important economic center in China, but also one of the major regions contributing to China's carbon emissions. Revealing the spatial distribution between carbon emissions and economic growth is essential for the formulation of low-carbon development policies. Following the principle from macro to micro, this paper investigates the spatial evolution trend and distribution characteristics between carbon emissions and economic growth in the Beijing-Tianjin-Hebei region from 2005 to 2020 by applying imbalance index model, the rank-scale rule, and decoupling index model. The results show that the imbalance index of carbon emissions decreased between 0.0601 and 0.0533 in a fluctuating way, indicating that the imbalance of spatial distribution of carbon emissions decreases. The imbalance index of economic growth increased between 0.0738 and 0.0851, indicating that economic growth has become more disequilibrated, and the spatial evolution of carbon emissions is not coordinated with economic growth. The Zipf dimension of carbon emissions declined from 1.1806 in 2005 to 0.9594 in 2020, and carbon emissions declined in big cities and increased in cities of the middle order. The Zipf dimension of economic growth increased from 1.1384 in 2005 to 1.2388 in 2020, and the economic growth monopoly in big cities increased. The decoupling coefficient of carbon emissions to economic growth declined, and the driving effect of economic growth on carbon emissions diminished. It is recommended that the Beijing-Tianjin-Hebei region should coordinate the allocation of factors and coordinate industrial adjustment. Hebei should accelerate industrial upgrading and establish a diversified industrial system.

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Exploring ecosystem health and its influencing factors is of great significance for promoting regional sustainable development. An ecosystem health assessment model was constructed, and the spatial-temporal evolution characteristics of ecosystem health in the Beijing-Tianjin-Hebei Region in 2000, 2010, and 2020 were analyzed. The geographical detector and GWR were used to identify the dominant factors affecting ecosystem health. The main conclusions were as follows:during the study period, the index of ecosystem natural health in the Beijing-Tianjin-Hebei Region was generally better in the north and west than that in the southeast, and it showed an overall upward trend. The index of ecosystem services in the Beijing-Tianjin-Hebei Region presented as a spatial differentiation pattern of high in the north and low in the south, and it showed a downward trend. The ecosystem health level in the Beijing-Tianjin-Hebei Region showed a trend of rising first and then declining, showing significant heterogeneity in spatial distribution. The ecological health level in the central urban area of large cities was mostly poor, and the ecosystem health level in the Yanshan and Taihang Mountains and Bohai Rim was better. During the study period, the spatial pattern of ecosystem health in the Beijing-Tianjin-Hebei Region remained relatively stable. The hot spots and sub-hot spots were mainly distributed in the northern mountainous areas of Hebei Province and the Taihang Mountains, and the cold spots and sub-cold spots were mainly distributed in the southeast plain and the surrounding areas of some big cities. Population density, annual average temperature, per capita cultivated land area, and urbanization level were the dominant factors of ecosystem health in the Beijing-Tianjin-Hebei Region, and they were all negatively correlated with ecosystem health.

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As the world's greatest energy consumer, China's energy consumption and transition have become a focus of attention. The most significant location for regional integration in the north of China is the Beijing-Tianjin-Hebei region, where the industrial sector dominates its energy consumption. Forecasting the energy demand and structure of industrial sectors in China's Beijing-Tianjin-Hebei region may help to promote the energy transition and CO2 emission mitigation. This study conducts a model based on the year 2020 using the Long-Range Energy Alternatives Planning System (LEAP) software and sets two scenarios (baseline scenario and emission peak scenario) to forecast the future energy demand and CO2 emissions of industrial sectors in China's Beijing-Tianjin-Hebei region until the year 2035. Moreover, the industrial sectors are classified into traditional high-energy-consuming industries, emerging manufacturing industries, daily-related light industries, and other industries. The forecasting results show that (1) The industrial energy demand of the entire Beijing-Tianjin-Hebei region will grow from 234 Mtce in 2020 to 317 Mtce in 2035, and the corresponding energy structure will shift from coal-based to electricity-based; (2) at the provincial level, all three provinces will experience an increase in industrial energy demand between 2020 and 2035, with Hebei experiencing the fastest average annual growth rate of 2.18% and the largest share of over 80%, and Beijing experiencing the highest average annual electrification rate of 70%; (3) at the industrial sector level, the electricity and natural gas will gradually replace other energy sources as the main energy source for industry. The most representative industrial sub-sector in Beijing, Tianjin, and Hebei provinces are all traditional high-energy-consuming industries, which will account for more than 90% of the total energy demand in both Tianjin and Hebei by 2035.

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To accurately assess the health benefits of the coal-to-electricity policy during the heating period in the Beijing-Tianjin-Hebei(BTH) Region, the premature deaths caused by PM2.5 before and after the implementation of the coal-to-electricity policy during the heating period in each district and county of the BTH Region were estimated, and the corresponding health loss values were calculated using the willingness to pay method. The results showed that the implementation of the coal-to-electricity policy in the BTH Region brought 1745 cases(95% CI:1443-1907) of health benefits and 2.38 billion yuan(95% CI:1.45-3.06) in economic benefits. In Beijing, Tianjin, and Hebei there were 495 cases(95% CI:436-554), 296 cases(95% CI:238-354), and 954 cases(95% CI:693-1076) of health benefits, respectively. The economic benefits were 0.35 billion yuan(95% CI:0.30-0.39), 0.33 billion yuan(95% CI:0.27-0.40), and 1.70 billion yuan(95% CI:0.88-2.28), respectively, accounting for 0.01%, 0.02%, and 0.04% of GDP in each region. The number of premature deaths due to COPD, LC, ALRI, IHD, and STROKE decreased by 187 cases(95% CI:165-224), 318 cases(95% CI:178-458), 193 cases(95% CI:115-204), 506 cases(95% CI:232-780), and 542 cases(95% CI:463-621), respectively. Areas with relatively high environmental PM2.5 concentrations and concentrated population-intensive pollution emissions can achieve significant health and economic benefits.

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Vehicle exhaust emissions are posing an increasingly adverse impact on urban air quality. The emission characteristics analysis and health effect assessment of specific air pollution sources can provide scientific evidence for environmental air quality management. The characteristics and health effects of PM2.5 emissions from vehicles and economic losses caused by them in the Beijing-Tianjin-Hebei Region were analyzed from 2010 to 2020. From 2010 to 2020, PM2.5 emissions from vehicles in the Beijing-Tianjin-Hebei Region showed an annual increase at first, followed by a slow decrease. According to the emission sharing ratios of different vehicle types, heavy-duty trucks and buses were the main contributors to PM2.5, with a total contribution rate of over 65.27%. The emission characteristics of vehicle pollutants varied in different cities. The contribution rate of pollutants in Beijing decreased significantly, and the emission reduction in other cities was also dramatic. The evaluation results of the impact of PM2.5 emissions from vehicles on human health showed that the number of health endpoints in the Beijing-Tianjin-Hebei Region was on the rise. In 2020, PM2.5 pollution caused approximately 34337 premature deaths (95% CI:9025-57209), 45500 hospitalizations (95% CI:10800-80200), 282300 outpatients (95% CI:140500-416300), and 439000 people to fall ill (95% CI:160300-679200). Beijing had the largest number of patients that presented different health endpoints. The total health and economic losses caused by PM2.5 emissions from vehicles in 2010, 2015, and 2020 were 27.742 billion yuan (95% CI:8.616-44.643 billion yuan), 90.608 billion yuan (95% CI:28.476-144.050 billion yuan), and 129.965 billion yuan (95% CI:40.829-205.245 billion yuan), respectively. In addition, due to the differences in vehicle ownership, PM2.5 concentrations, population, and economic losses per case of health outcome, the health effects and economic losses varied in different cities within the region. Among these cities, Beijing, Tianjin, Baoding, and Tangshan were at higher health risks and suffered more economic losses. The results of this study will help reduce the adverse effects on health and economic losses caused by pollution discharge and provide scientific evidence for environmental protection authorities to implement targeted pollution prevention and control.

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From January 31st to February 20th, 2015 to 2022, the spatio-temporal characteristics of the PM2.5 pollution evolution of 44 cities in the Beijing-Tianjin-Hebei Region and its surrounding areas were analyzed. The contribution of simultaneous meteorology, coordinated emission reduction, and cross-regional transmission to PM2.5 concentration and chemical component changes were quantified, respectively, with the aim to provide scientific reference for regional air quality joint prevention and control under adverse meteorological conditions. The results showed that the mean value of PM2.5 concentration of 44 cities in 2022 was the lowest(46 µg·m-3) without heavy pollution in the same period of the lunar calendar since 2015, whereas the proportion of days with good air quality was the highest(83.3%). PM2.5 pollution was more serious in the southern region than that in the northern region, and the high values were mainly concentrated along the Taihang Mountains and Yanshan transmission channel cities. In 2016, under the unregulated emission of fireworks in the Spring Festival, the proportion of unpolluted days was 93.5%, which means that the strong atmospheric diffusion ability was crucial to improve air quality. In 2022, the static weather index(SWI) increased by 2.1 compared with that in 2021, indicating unfavorable atmospheric diffusion capacity. The average and peak values of PM2.5 decreased by 14 µg·m-3 and 76 µg·m-3, respectively. The reduction in PM2.5 concentration in Beijing owing to emission reduction measures increased by 96% compared with that before one month. Under the adverse atmospheric background in the Shanxi-Shandong-Henan Region, which contributed to the increase in PM2.5 concentration, the peak value of PM2.5 concentration decreased by 87 µg·m-3, indicating that the peak clipping effect of regional collaborative pollution emission reduction was significant. From 2019 to 2022, the concentration of PM2.5 chemical components decreased yearly with narrowed spatial differences, and the high value was concentrated in the central and southern regions. The descending order of PM2.5 secondary component concentration decrease was as follows:organic matter(21.9%)>sulfate(20%)>ammonium salt(16.7%)>nitrate(9.7%). The proportion of nitrate in PM2.5 increased yearly to 30.1%, indicating that the contribution of traffic pollution was relatively prominent. In 2022, the SNA concentration in the Shanxi-Shandong-Henan Region increased. Pollution from external sources accounted for the main contribution in the Beijing-Tianjin-Hebei Region(>50%). Liaoning Province contributed significantly to the PM2.5 concentration in cities along the Bohai(>10%), and nitrate and sulfate were easy to transport over long distances. It is necessary to strengthen the coordinated control of precursors of secondary chemical components SO2, NOx, and NH3.

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In recent years, the ozone (O3) volume fraction in the Beijing-Tianjin-Hebei Region in summer have remained high, light to moderate pollution occurs frequently, and research on related response mechanisms is urgently needed. This study applied the WRF-Chem model to simulate the change in ozone volume fraction in this region by setting 13 precursor emission scenarios in a representative month in the summer of 2018. The results revealed that VOC-sensitivity and no-sensitivity regimes commonly occurred in the Beijing-Tianjin-Hebei Region in July, and the VOC-sensitivity regimes were mainly accumulated in the central Beijing-Tianjin-Hebei Region, with a north-to-south zonal distribution and an area share of 15.60%-26.59%. The relative response intensity (RRI) of O3 volume fraction to precursor emissions in urban areas had large spatial variability, with RRI_VOC and RRI_NOx in the ranges of 0.03-0.16 and -0.40-0.03, respectively. The higher the latitude of urban areas, the more dramatic were the RRI values, indicating a more significant regional transport influence. The lower RRI_NOx values in urban areas with high intensity of precursor emissions implied a negative dependence of RRI_NOx on local NO2 concentrations; however, RRI_VOC was not significantly correlated with NO2levels and was more dependent on the relative abundance of precursors (VOCs:NOx). The ratio of RRI_VOC to RRI_NOx showed negative values in majority of the cities; therefore, collaborative VOCs emission reduction is necessary to suppress the deterioration of O3 volume fraction. The absolute value of this ratio was much lower in cities with high industrialization and urbanization than in ordinary small and medium-sized cities, implying that the demand for collaborative VOCs emission reduction in these cities will be higher. However, even under 50% reduction of precursors, the improvement in O3 volume fraction was limited in regional cities, and the combined prevention in neighboring cities remains important.

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The systematic decline of rural areas in the process of rapid urbanization has become a global trend, creating greater challenges for sustainable rural development. As the spatial projection of socio-economic development and living environment in rural areas, the continuous tracking of rural settlements (RUS) is crucial to quantify the imbalance of rural development. However, consistent information on RUS is highly needed but is quite deficient in current research. In this study, a cost-effective mapping model was proposed to produce an annual RUS dataset in the rapid urbanization region of Beijing-Tianjin-Hebei (BTH) in North China during 1990-2020, and the temporal-spatial regularity of RUS changes was further analyzed. The location-based and the area-based comparison verified the effectiveness of our model, with a mean overall accuracy of 85% and a mean correlation value of 0.88, respectively. The total area of RUS in the BTH region increased by 2561 km2 from 1990 to 2020, while the average size of RUS remained stable after 2005. The annual change trends in RUS appeared with increasing and decreasing accounting for 76.33% and 23.67%, respectively. The centroids of RUS in Tianjin and Hebei have moved closer to Beijing, while those in Beijing have moved away from the former. Notably, we have identified 56.3% counties in the BTH region belong to the "Convex-I" change type in RUS. In general, our work can help to consistently quantify the spatiotemporal patterns of RUS in a cost-effective way, providing more explicit spatial information and continuous temporal information for rural residential land management.

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Ecological compensation plays an important role in maintaining ecosystem services and promoting regional green development. We built a regional horizontal ecological compensation model based on water resources pattern and insurance gain, and which was used to solve the problems of single compensation method and low compensation efficiency. Taking the Beijing-Tianjin-Hebei region as an example, we analyzed water footprint and water ecological carrying capacity from 2000 to 2019. The compensation subject and object and water footprint compensation amount were determined according to the input cost of ecological protection and allocation factor. Then, the insurance pricing model was introduced to determine ecological insurance premium rate. We calculated insurance compensation, ecological compensation standard and different types of ecosystem service value. Results showed that the whole region was at a state of water ecological deficit, with the agricultural water footprint accounting for 94.5%. From the perspective of the compensation subject and object, Beijing and Tianjin, as the compensation subject, needed to pay 0.402 billion yuan and 0.396 billion yuan (the amount of compensation) to Hebei Province each year. Hebei Province obtained a total of 0.228 billion yuan of ecological insurance with an insurance premium rate of 1.4%, and should receive an average annual ecological compensation standard of 0.81 billion yuan from Beijing and Tianjin. Hydrological regulation was the core ecosystem service in the region, with an average value of 187.974 billion yuan. It was of strategic significance to introduce ecological insurance mechanism to construct horizontal ecological compensation mechanism, improve ecosystem service function, and enhance the value of ecosystem services in the study area.

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To achieve low-carbon development of industrial land in China, it is important to coordinate the conflicts of interest among stakeholders in the process of land expropriation and utilization. However, the complex interaction mechanism and influencing factors among stakeholders make it difficult to achieve the goal under the Chinese decentralization and unique land development pattern. To solve these problems, this paper first analyzes the four main stakeholders' conflicts of interest in the process of land expropriation and utilization, that is, the central government, local government, peasant, and enterprise. Then, we construct two evolutionary game models to examine the dynamic changes of stakeholders' different strategies and take the Beijing-Tianjin-Hebei region as an example to compare the impacts of factors on strategies under different conditions using simulation analysis. The research shows that under the Chinese decentralization, adjusting the action strengths of different stakeholders can have different effects on system equilibrium. In terms of the central government's reward and penalty, increasing the reward and penalty for local government will shorten the time of system equilibrium to a different degree, and there is little difference between the effects of political and economic penalties. Interestingly, increasing the incentives for peasants cannot promote the system equilibrium in advance. In addition, the key to local governments' decision on illegal land expropriation lies in benefits rather than costs, and investment in low-carbon technology reform with positive externalities is easier to control than investment in economic production with negative externalities associated with pollution emissions.

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Achieving the "double carbon" goal is a major task and challenge facing China. The emission reduction actions in typical urban agglomerations are of great significance. Based on the consideration of the impact of regional coordinated development, this study analyzed influencing factors and conducted prediction of carbon emissions from terminal energy consumption in the Beijing-Tianjin-Hebei (BTH) region. Firstly, the factors affecting carbon emissions were screened through the STIRPAT model. Then, the paper designs different scenarios and finally uses the genetic algorithm extreme learning machine (GA-ELM) algorithm to predict the carbon emissions of the BTH region, with and without considering the impact of the coordinated development strategy. The research shows that the increase in energy intensity and the improvement of energy consumption structure have the largest promotion effect on carbon emission reduction. At the same time, the significant role of the coordinated development strategy in promoting regional carbon emission reduction was verified. Therefore, the BTH region should adhere to the path of coordinated development, innovate low-carbon technology, and deepen the concept of green consumption to promote the realization of regional carbon emission reduction goals.

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Under the "Digital China" strategy and "Carbon Peaking and Carbon Neutrality" goal, it is significant to explore the carbon reduction effect from the digital economy development in a multi-dimensional way. Based on the panel data of 13 cities in the Beijing-Tianjin-Hebei (BTH) region from 2011 to 2019, this study uses mechanism test model, threshold effect model, and spatial Durbin model which empirically test the influence mechanism and spatial spillover effect of digital economy development on regional CEI. The research found that (1) the digital economy development in the BTH region can reduce regional CEI, and it passes the endogenous test; (2) the digital economy indexes of 13 cities in the BTH region have significantly increased with time evolution, but there is obvious spatial unevenness; the CEI of each city except Tianjin decreases significantly with time evolution, and Tianjin shows a trend of decreasing and then increasing; (3) digital economy has a positive spatial correlation, showing the characteristics of "H-H" and "L-L" clustering. Furthermore, the digital economy has a spatial spillover effect on the CEI of neighboring cities; (4) the digital economy development can promote the industrial structure rationalization and upgrade, improves the urban green innovation quantity and quality, then reduces the regional CEI through them; and (5) the impact strength of digital economy on CEI varies at different threshold intervals of the mechanism variable.

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