Search Results (832)

The phenomenon of arable land non-agriculturalization has become increasingly severe, posing significant threats to the security of arable land resources and ecological sustainability. This study focuses on Dachang Hui Autonomous County in Langfang City, Hebei Province, a region located at the edge of the Beijing–Tianjin–Hebei metropolitan cluster. In recent years, the area has undergone accelerated urbanization and industrial transfer, resulting in drastic land use changes and a pronounced contradiction between arable land protection and the expansion of construction land. The study period is 2016–2023, which covers the key period of the Beijing–Tianjin–Hebei synergistic development strategy and the strengthening of the national arable land protection policy, and is able to comprehensively reflect the dynamic changes of arable land non-agriculturalization under the policy and urbanization process. Multi-temporal Sentinel-2 imagery was utilized to construct a multi-dimensional feature set, and machine learning classifiers were applied to identify arable land non-agriculturalization with optimized performance. GIS-based analysis and the geographic detector model were employed to reveal the spatio-temporal dynamics and driving mechanisms. The results demonstrate that the XGBoost model, optimized using Bayesian parameter tuning, achieved the highest classification accuracy (overall accuracy = 0.94) among the four classifiers, indicating its superior suitability for identifying arable land non-agriculturalization using multi-temporal remote sensing imagery. Spatio-temporal analysis revealed that non-agriculturalization expanded rapidly between 2016 and 2020, followed by a deceleration after 2020, exhibiting a pattern of “rapid growth–slowing down–partial regression”. Further analysis using the geographic detector revealed that socioeconomic factors are the primary drivers of arable land non-agriculturalization in Dachang Hui Autonomous County, while natural factors exerted relatively weaker effects. These findings provide technical support and scientific evidence for dynamic monitoring and policy formulation regarding arable land under urbanization, offering significant theoretical and practical implications. Full article

As urbanization processes are no longer characterized by simple linear expansion but exhibit leaping, edge-sparse, and discontinuous features, spatiotemporally continuous impervious surface coverage data are needed to better characterize urbanization processes. This study utilized GAIA impervious surface binary data and employed spatiotemporal aggregation methods to convert thirty years of 30 m resolution data into 1 km resolution spatiotemporal impervious surface coverage data, constructing a long-term time series annual impervious surface coverage dataset for the Beijing–Tianjin–Hebei region. Based on this dataset, we analyzed urban expansion processes and landscape pattern indices in the Beijing–Tianjin–Hebei region, exploring the spatiotemporal response relationships of ecological environment changes. Results revealed that the impervious surface area increased dramatically from 7579.3 km² in 1985 to 37,484.0 km² in 2020, representing a year-on-year growth of 88.5%. Urban expansion rates showed two distinct peaks: 800 km²/year around 1990 and approximately 1700 km²/year during 2010–2015. In high-density urbanized areas with impervious surfaces, the average forest area significantly increased from approximately 2500 km² to 7000 km² during 1985–2005 before rapidly declining, grassland patch fragmentation intensified, while in low-density areas, grassland area showed fluctuating decline with poor ecosystem stability. Furthermore, by incorporating natural and social factors such as Fractional Vegetation Coverage (FVC), Habitat Quality Index (HQI), Land Surface Temperature (LST), slope, and population density, we assessed the vulnerability of urbanization development in the Beijing–Tianjin–Hebei region. Results showed that high vulnerability areas (EVI > 0.5) in the Beijing–Tianjin core region continue to expand, while the proportion of low vulnerability areas (EVI < 0.25) in the northern mountainous regions decreased by 4.2% in 2020 compared to 2005. This study provides scientific support for the sustainable development of the Beijing–Tianjin–Hebei urban agglomeration, suggesting location-specific and differentiated regulation of urbanization processes to reduce ecological risks. Full article

This study employs k-means clustering to group 30 Chinese provinces into four CO₂ emission patterns, characterized by increasing emission levels and distinct energy consumption structures, and captures their dynamic evolution from 2000 to 2021 using a discrete endogenous Markov chain approach. While Shanghai, Jiangxi, and Hebei retained their original classifications, provinces such as Beijing, Fujian, Tianjin, and Anhui transitioned from higher to lower emission patterns, indicating notable reversals in emission trajectories. To identify the determinants of these transitions, GDP growth rate, population growth rate, and energy investment are incorporated as time varying covariates. The empirical findings demonstrate that GDP growth substantially increases interpattern mobility, thereby weakening state persistence, whereas population growth and energy investment tend to reinforce emission pattern stability. These results imply that policy responses must be tailored to regional dynamics. In rapidly growing regions, fiscal incentives and technological upgrading may facilitate downward transitions in emission states, whereas in provinces where emissions remain persistent due to demographic or investment related rigidity, structural adjustments and long term mitigation frameworks are essential. The study underscores the importance of integrating economic, demographic, and investment characteristics into carbon reduction strategies through a region specific and data informed approach. Full article

As an environmental policy, the Action Plan of Atmosphere Pollution Control in Beijing-Tianjin-Hebei and Surrounding Areas in Autumn and Winter (Action Plan of APC) was implemented in 2017, with the goal of achieving the sustainable growth of the regional economy. This study examines the effect of the Action Plan of APC on green total factor productivity (GTFP) in the Chinese construction industry employing a difference-in-differences (DID) approach. The findings indicate the following: Firstly, the environmental policy of the Action Plan of APC has significantly improved the GTFP of the aforementioned areas, and the result is still valid after robustness testing; secondly, the dynamic effect testing reveals that the influence follows an increasing trend over time; thirdly, due to the different degrees of marketization, the influence of the Action Plan of APC on GTFP in Chinese construction industry exhibits notable regional heterogeneity. From the perspectives of both the government and enterprises, this study offers recommendations for promoting the GTFP of China’s construction industry. It also provides a novel framework for assessing the effect of environmental policies on the GTFP of the Chinese construction industry. Full article

Urban–rural integration realises the coordinated development and prosperity of urban and rural areas as a whole by optimising the allocation of resources and the flow of factors, and its connotations have been extended from a single dimension to multiple dimensions such as people, land and industry. The Beijing–Tianjin–Hebei Region has a typical “Core–Periphery Structure”, and this paper took the 187 county units within the region as the research object, taking into account indicators of development and coordination to construct an evaluation index system of urban–rural integration of the Beijing–Tianjin–Hebei region counties in the dimensions of “people–land–industry”. Global principal component analysis was used to measure the evolutionary pattern of the urban–rural integration level between 2005 and 2020, and its spatiotemporal drivers were analysed by using the Geographical and Temporal Weighted Regression model (GTWR). The results of the study show that (1) the level of urban–rural integration in the Beijing–Tianjin–Hebei region showed an increasing trend during the 15-year study period, the high-value areas of urban–rural integration were mainly distributed in Beijing and the Bohai Rim region in the eastern part of the Tianjin–Hebei region, and the level of urban–rural integration of the peri-urban county units of the city was better than that of the remote counties and cities as a whole. (2) In terms of spatial agglomeration, all dimensions were characterised by significant spatial agglomeration. The degree of agglomeration was categorised as urban–rural comprehensive integration (U-RCI) > urban–rural industry integration (U-RII) > urban–rural land integration (U-RLI) > urban–rural people integration (U-RPI). (3) In terms of spatial and temporal driving factors for urban–rural integration, the driving role of U-RPI, U-RLI and U-RII for U-RCI has gradually weakened during the past 15 years, and urban–rural integration in the counties shifted from a single role to a more central coordinated and multidimensional driving role. Full article

Anoplophora glabripennis (Asian longhorned beetle, ALB) (Motschulsky, 1854) is a local forest pest in China. Although the suitable area for this pest has some research history, it does not accurately predict the future distribution area of ALB. Accurate prediction of its suitable area can help control the harm caused by ALB more effectively. In this study, we applied the maximum entropy model to predict the suitable area for ALB. Moreover, the prediction results revealed that ALB is distributed mainly in northern, eastern, central, southern, southwestern, and northwestern China, and its high-fit areas are located mainly in northern, northwestern, and southwestern China. The average minimum temperature in September, precipitation seasonality (coefficient of variation), the average maximum temperature in April, and average precipitation in October had the greatest influence on ALB. The greatest distribution probabilities were observed at the September average minimum temperature of 16 °C, the precipitation seasonality (coefficient of variation) of 130%, the April average maximum temperature of 14 °C, and the October average precipitation of 30 mm. Furthermore, with climate change, the non-suitability area for the ALB will show a decreasing trend in the future. The intermediate suitability area will increase, while the low and high suitability areas will first increase and then decrease. Taken together, the potentially suitable areas for ALB in China include the Beijing–Tianjin–Hebei region and the Shanghai region in North China and East China, providing a deeper understanding of ALB control. Full article

Urban and regional ecosystems are increasingly challenged by the compounded effects of climate change and intensive land use. In this study, a predictive assessment framework for ecosystem resilience in the Beijing–Tianjin–Hebei region was developed by integrating multi-source remote sensing data, with the aim of quantitatively evaluating the coupled effects of climate change and land use change on future ecosystem resilience. In the first stage of the study, the SD-PLUS coupled modeling framework was employed to simulate land use patterns for the years 2030 and 2060 under three representative combinations of Shared Socioeconomic Pathways and Representative Concentration Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5). Building upon these simulations, ecosystem resilience was comprehensively evaluated and predicted on the basis of three key attributes: resistance, adaptability, and recovery. This enabled a quantitative investigation of the spatio-temporal dynamics of ecosystem resilience under each scenario. The results reveal the following: (1) Temporally, ecosystem resilience exhibited a staged pattern of change. From 2020 to 2030, an increasing trend was observed only under the SSP1-2.6 scenario, whereas, from 2030 to 2060, resilience generally increased in all scenarios. (2) In terms of scenario comparison, ecosystem resilience typically followed a gradient pattern of SSP1-2.6 > SSP2-4.5 > SSP5-8.5. However, in 2060, a notable reversal occurred, with the highest resilience recorded under the SSP5-8.5 scenario. (3) Spatially, areas with high ecosystem resilience were primarily distributed in mountainous regions, while the southeastern plains and coastal zones consistently exhibited lower resilience levels. The results indicate that climate and land use changes jointly influence ecosystem resilience. Rainfall and temperature, as key climate drivers, not only affect land use dynamics but also play a crucial role in regulating ecosystem services and ecological processes. Under extreme scenarios such as SSP5-8.5, these factors may trigger nonlinear responses in ecosystem resilience. Meanwhile, land use restructuring further shapes resilience patterns by altering landscape configurations and recovery mechanisms. Our findings highlight the role of climate and land use in reshaping ecological structure, function, and services. This study offers scientific support for assessing and managing regional ecosystem resilience and informs adaptive urban governance in the face of future climate and land use uncertainty, promotes the sustainable development of ecosystems, and expands the applicability of remote sensing in dynamic ecological monitoring and predictive analysis. Full article

The inter-provincial border region in eastern China, encompassing the junction of Jiangsu, Anhui, Shandong, and Henan provinces, serves as a crucial zone that connects the important economic zones of Beijing–Tianjin–Hebei and the Yangtze River Delta. It is of great significance to study the temporal variation characteristics, spatial distribution patterns, and driving factors of PM_2.5 concentrations in this region. Based on the PM_2.5 concentration observation data, ground meteorological data, environmental data, and socio-economic data from 2022 to 2024, this study conducted in-depth and systematic research by using advanced methods, such as spatial autocorrelation analysis and geographical detectors. The research results show that the concentration of PM_2.5 rose from 2022 to 2023, but decreased from 2023 to 2024. From the perspective of seasonal variations, the concentration of PM_2.5 shows a distinct characteristic of being “high in winter and low in summer”. The monthly variation shows a “U”-shaped distribution pattern. In terms of spatial changes, the PM_2.5 concentration in the inter-provincial border region of eastern China (Jiangsu, Anhui, Shandong, Henan) forms a gradient difference of “higher in the west and lower in the east”. The high-concentration agglomeration areas are mainly concentrated in the Henan part of the study region, while the low-concentration agglomeration areas are distributed in the eastern coastal parts of the study region. The analysis of the driving factors of the PM_2.5 concentration based on geographical detectors reveals that the average temperature is the main factor affecting the PM_2.5 concentration. The interaction among the factors contributing to the spatial differentiation of the PM_2.5 concentration is very obvious. Temperature and population density (q = 0.92), temperature and precipitation (q = 0.95), slope and precipitation (q = 0.97), as well as DEM and population density (q = 0.96), are the main combinations of factors that have continuously affected the spatial differentiation of the PM_2.5 concentration for many years. The research results from this study provide a scientific basis and decision support for the prevention, control, and governance of PM_2.5 pollution. Full article

Battery-swapping stations (BSSs) are pivotal for supplying energy to electric heavy-duty trucks. However, their operations face challenges in accurate demand forecasting for battery-swapping and fair revenue allocation. This study proposes an optimization strategy for the economic operation of BSSs that optimizes revenue allocation and load balancing to enhance financial viability and grid stability. First, factors including geographical environment, traffic conditions, and truck characteristics are incorporated to simulate swapping behaviors, supporting the construction of an accurate demand-forecasting model. Second, an optimization problem is formulated to maximize the weighted difference between BSS revenue and squared load deviations. An economic operations strategy is proposed based on an adaptive Shapley value. It enables precise evaluation of differentiated member contributions through dynamic adjustment of bias weights in revenue allocation for a strategy that aligns with the interests of multiple stakeholders and market dynamics. Simulation results validate the superior performance of the proposed algorithm in revenue maximization, peak shaving, and valley filling. Full article

The Jidong Oilfield No. 2 Underground Gas Storage (UGS), located in an active fault zone in Northern China, is a key facility for ensuring natural gas supply and peak regulation in the Beijing–Tianjin–Hebei region. To evaluate the effectiveness of a combined surface and shallow borehole monitoring system under deep reservoir conditions, a 90-day microseismic monitoring trial was conducted over a full injection cycle using 16 surface stations and 1 shallow borehole station. A total of 35 low-magnitude microseismic events were identified and located using beamforming techniques. Results show that event frequency correlates positively with wellhead pressure variations instead of the injection volume, suggesting that stress perturbations predominantly control microseismic triggering. Events were mainly concentrated near the bottom of injection wells, with an average location error of approximately 87.5 m and generally shallow focal depths, revealing limitations in vertical resolution. To enhance long-term monitoring performance, this study recommends deploying geophones closer to the reservoir, constructing a 3D velocity model, applying AI-based phase picking, expanding array coverage, and developing a microseismic-injection coupling early warning system. These findings provide technical guidance for the design and deployment of long-term monitoring systems for deep reservoir conversions into UGS facilities. Full article

The Beijing–Tianjin–Hebei (BTH) region is one of China’s most water-scarce yet economically vital areas, facing increasing challenges due to climate change and intensive human activities. This study develops an integrated Water–Food–Energy–Ecology (WFEE) simulation and regulation model to assess the system’s stability under coordinated development scenarios and extreme climate stress. A 500-year precipitation series was reconstructed using historical drought and flood records combined with wavelet analysis and machine learning models (Random Forest and Support Vector Regression). Results show that during the reconstructed historical megadrought (1633–1647), with average precipitation anomalies reaching −20% to −27%, leading to a regional water shortage rate of 16.9%, food self-sufficiency as low as 44.7%, and a critical reduction in ecological river discharge. Under future recommended scenario with enhanced water conservation, reclaimed water reuse, and expanded inter-basin transfers, the region could maintain a water shortage rate of 2.6%, achieve 69.3% food self-sufficiency, and support ecological water demand. However, long-term water resource degradation could still reduce food self-sufficiency to 62.9% and ecological outflows by 20%. The findings provide insights into adaptive water management, highlight the vulnerability of highly coupled systems to prolonged droughts, and support regional policy decisions on resilience-oriented water infrastructure planning. Full article

By assuming that both travel and departure time are normally distributed variables, a multi-objective stochastic optimization model for the multi-airport flight cooperative scheduling problem (MAFCSP) with CvaR of travel and departure time is firstly proposed. Herein, conflicts of flights from different airports at the same waypoint can be avoided by simultaneously assigning an optimal route to each flight between the airport and waypoint and determining its practical departure time. Furthermore, several real-world constraints, including the safe interval between any two aircraft at the same waypoint and the maximum allowable delay for each flight, have been incorporated into the proposed model. The primary objective is minimization of both total carbon emissions and delay times for all flights across all airports. A feasible set of non-dominated solutions were obtained using a two-stage heuristic approach-based NSGA-II. Finally, we present a case study of four airports and three waypoints in the Beijing–Tianjin–Hebei region of China to test our study. Full article

Water resources are strategic resources that support regional economic social development and maintain the health and stability of ecosystems. This study revealed the evolution of the coordinated development of China’s water resources–economic society–ecological environment system based on the coordination degree mode. The research was conducted by integrating machine learning with traditional mathematical methods; by setting up the status quo development scenario, water resources priority scenario, economic society priority scenario, ecological environment priority scenario and balanced development scenario; and by using the Holt exponential smoothing–feedforward neural network prediction model, the coordinated development trends under different scenarios were predicted. The results showed that, analyzed from the perspective of the coordinated evolution type of the dual systems, the dominant development system during the study period gradually transformed from water resources–economic society to water resources–ecological environment. For the coordinated development of the complex system, the coordination degree showed “stepped leap—resilient fluctuation (from 0.7242 to 0.8238)”, and “better in the southeast than in the northwest, with significant advantages in the coast”. The most significant increase in the coordination degrees were observed in the balanced development scenario and economic society priority scenarios, where it increased by an average of around 5%, confirming the effective contribution of stable economic and social development to the level of coordination. This study provides theoretical support and practical guidance for regional water resources management. Full article

This study examines the impact of the Beijing–Tianjin–Hebei (BTH) coordinated development policy on the regional industrial network structure, with a focus on the significance of electricity consumption data in social network analysis (SNA). Utilizing a gravity model integrated with electricity consumption data, this research employs centrality analysis and Lambda analysis to compare changes in the steel industry network before and after policy implementation. The findings reveal that traditional models relying solely on indicators such as population and Gross Domestic Product (GDP) fail to comprehensively capture regional economic linkages, whereas incorporating electricity consumption data enhances the model’s accuracy in identifying core nodes and latent connections. Post policy implementation, the centrality of Beijing and Tianjin increased significantly, reflecting their transition from production hubs to centers for research and development (R&D) and management, while Shijiazhuang’s pivotal role diminished. This study also uncovers a “core–periphery” structure in the BTH urban network, where core cities (Beijing, Tianjin, and Shijiazhuang) dominate resource allocation and information flow, while peripheral cities exhibit uneven development. These results provide a scientific basis for optimizing regional coordinated development policies and underscore the critical role of electricity consumption data in refining regional economic analysis. Incorporating electricity consumption data into the gravity model significantly enhances its explanatory power by capturing hidden economic ties and improving policy evaluation, offering a more accurate and dynamic assessment of regional industrial linkages. Full article

The promotion of new energy vehicles (NEVs) represents a critical strategy for mitigating carbon emissions and air pollution. To evaluate the CO₂ and air pollutant reduction potential of NEVs in the Beijing–Tianjin–Hebei region, this study developed an integrated framework combining gray correlation analysis (GRA) and bidirectional long short-term memory (BiLSTM), referred to as the GRA-BiLSTM model, to forecast the adoption trend of NEVs and calculate the CO₂ and air pollutant emission reduction. The GRA-BiLSTM model developed in this study shows optimal predictive performance. The results indicate that new energy vehicles (NEVs) have great potential for environmental collaborative emission reduction in the transportation sector: it is predicted that by 2035, the total number of NEVs will be nearly 11.88 million, with a cumulative reduction of 2.76 billion tons of carbon emissions and significant reductions in various key air pollutants. This study provides an important quantitative basis for formulating pollution reduction and carbon reduction policies in the transportation sector. Full article