The black soil region of Northeast China (NEC) is a critical grain-producing area facing severe soil degradation characterized by topsoil thinning, nutrient depletion, and acidification. However, the long-term spatiotemporal evolution of soil nutrients and the quantitative attribution of their drivers over the past four decades remain under-explored. Focusing on typical state farms in the Sanjiang Plain (SJP), this study utilized a dataset spanning the 1980s, 2010s, and 2020s, combining historical records with field profile sampling. Piecewise Structural Equation Modeling (PSEM) was employed to disentangle the relative contributions of climatic conditions, geographic factors, environmental attributes, and agricultural management to soil nutrient dynamics. Results show: (1) From the 1980s to the 2020s, significant acidification was observed, with soil pH decreasing by 0.54 units. Soil organic matter (SOM), total nitrogen (TN), and total potassium (TK) declined by 52.67%, 8.05%, and 3.07%, respectively, whereas total phosphorus (TP) increased by 16.11%, indicating a nutrient imbalance. (2) Temporal heterogeneity was evident: SOM and TK depletion dominated the first phase (1980s–2010s), while rapid acidification and TP accumulation characterized the second phase (2010s–2020s). Spatially, acidification was most severe in the northwestern SJP, while the central region experienced a rapid SOM decline. (3) The PSEM analysis revealed divergent driving mechanisms: Climatic conditions were the primary direct drivers of pH, TP, and TK variations. In contrast, geographic factors and agricultural management were the dominant drivers for TN. SOM dynamics were jointly regulated by climate, environmental attributes, and agricultural management. The results suggest a gradual transition from reclamation-induced nutrient depletion to degradation processes increasingly influenced by coupled climatic and anthropogenic factors.

The reuse potential of abandoned cropland is significantly affected by the duration of abandonment. Ignoring such variation may lead to inefficient resource allocation. However, a systematic understanding of how abandonment duration shapes reuse strategies and associated multi-objective benefits remains limited. To address this gap, we excluded ecological restoration areas and identified the spatial distribution of abandoned cropland of varying durations across China. We then applied an interpretable machine learning model to distinguish between recultivation and afforestation pathways and quantified their potential benefits in terms of food production and carbon storage. The results are as follows: (1) The total abandoned cropland area reached 126 thousand km2, mainly located in China's second topographic step. Both abandonment area and recultivation probability decreased with longer abandonment duration, with 52.2 thousand km2 abandoned for up to 10 years and 73.7 thousand km2 for 11 to 33 years. (2) Abandoned cropland can be reused through two pathways: 70.2 thousand km2 via recultivation and 34.5 thousand km2 via afforestation. (3) Reusing abandoned cropland could yield 26.3 million tons of food, enough to feed 66 million people, and sequester 570 million tons of carbon, equivalent to 16.6 % of China's annual emissions. Recultivation of short-term abandonment offers higher production gains, as the per-area yield potential declines with increasing abandonment duration. This study emphasizes that abandonment duration should be taken into account when formulating land use policies for abandoned cropland reuse. The findings provide valuable insights for enhancing food security and supporting carbon neutrality goals.

Disentangling the driving mechanisms of urban impervious surface (UIS) spatial changes is critical for developing effective urban growth planning. However, previous studies often overlooked the indirect effects of multiple factors especially inter-urban spatial interaction and policy factors on urban expansion in urban clusters. Here, we used structural equation modeling and geographically weighted regression to quantify the spatiotemporal patterns of direct and indirect effects of socioeconomic factors, geographical environment, inter-urban spatial interaction, and policy factors on urban expansion in the Beijing-Tianjin-Hebei (BTH) region from 1990 to 2020. The findings showed that urban population, inter-urban spatial interaction, tertiary industry, road density, and policies were the main drivers of UIS changes. Among them, inter-urban spatial interaction primarily had an indirect positive effect on urban spatial patterns by increasing urban population and optimizing industrial structures, with the strongest impact in southern BTH. Regional planning policies such as development zones promoted urban expansion by stimulating industrial development and attracting urban population, with their influence escalating from 0.41 in 1990 to 0.57 in 2010. These findings highlight the importance of strategically guiding inter-urban spatial interactions and optimizing industrial layouts to foster compact urban development and sustainable land use in the BTH region.

Soil texture is an important parameter representing the physical properties of soil, so accurate mapping of it is crucial for revealing the intrinsic soil properties. Using Sentinel-2 images from Youyi Farm, which is located in the third major black soil region of Northeast China, bare soil information, and crop growth information, the relationship between them and soil texture mapping accuracy under different annual climate patterns (2019-flooded, 2020-normal, and 2021-drought) was explored. The results indicated that (1) the highest mapping accuracy was obtained for sand, silt, and clay after the recursive feature elimination, with R & sup2; and RMSE values reaching 0.732/8.544%, 0.762/6.725%, and 0.612/1.925%, respectively. (2) In the flooded year, crop-growth information (NDVI, EVI) added in different months had a small effect on the mapping accuracy of clay, while sand and silt showed large fluctuations. (3) The results show that all bands of remote sensing imagery have major influences on all soil texture predictions and that crop growth information contributes relatively little to sand and silt but significantly influences clay predictions. This study offers new perspectives and methods for high-resolution mapping of soil texture and related soil property studies.

Understanding the three-dimensional structure of the urban heat island (UHI) is essential for climate-adaptive planning. Beyond surface and canopy-layer warming, UHIs involve vertically integrated heat accumulation; however, most studies assess temperature differences at a single height. This study infers vertical UHI coupling from the joint yet differentiated responses of surface (SUHII), canopy-layer (CUHII), and vertically integrated boundary-layer (VUHII) indicators, without directly quantifying coupling or simulating energy exchange. It further examines morphology-dependent relationships using morphology-zone classification and double machine learning (DML)-based statistical attribution. The results show that SUHII and CUHII exhibit continuous urban-rural gradients, whereas VUHII is more localized and fragmented. At night, mean VUHII reaches 963.5 K·m in dense central districts, compared with 206.6 K·m in peripheral plains, indicating that conventional surface- or canopy-layer metrics alone may not fully represent vertically integrated heat accumulation within the lower atmosphere. Thermal intensity generally follows high-rise compact (HC) > low-rise compact (LC) > low-rise sparse (LS) > low-rise open (LO). Nighttime VUHII trends are 59.1, 18.8, and 13.1 K·m·a⁻¹ in HC, LC, and LS zones, respectively, and winter nighttime VUHII exceeds 2500 K·m in HC zones. Transition-zone analysis indicates that VUHII changes lag behind those of SUHII and CUHII during urban-form evolution: during LS to HC transitions, VUHII remains 200–400 K·m, far below the 1500–2000 K·m observed in stable HC zones. After adjustment for observed covariates, DML-based statistical attribution indicates that land cover and surface properties have the strongest estimated conditional associations with SUHII and CUHII. In contrast, VUHII is more strongly associated with three-dimensional urban structure, atmospheric conditions, and human activity. At night, building-height heterogeneity, impervious-surface connectivity, and population density show positive conditional effects on VUHII, whereas vegetation shows a negative conditional effect. These findings provide evidence-informed planning priorities for morphology-aware mitigation of heat accumulation.

Changes in Arctic lake extent are critical indicators of regional water balance and permafrost stability. While previous studies have documented widespread lake shrinkage or disappearance under warming and permafrost thaw, it remains unclear whether lake geometric features-such as shoreline length, area, shape complexity, and depth-modulate the lake response to these drivers. This study examined lake area dynamics for more than 4000 lakes across the Yukon and Mackenzie basins using Landsat-derived annual lake area products from 2000 to 2020. We employed boosted regression trees to quantify the contributions of climate change and permafrost thaw to the observed trends and to evaluate the regulatory effect of lake geometric features. Our results revealed that while some lakes expanded, the average trend for individual lakes was characterized by reductions, with the mean rates of-0.20 ha/yr and-0.14 ha/yr in the Mackenzie and Yukon basins, respectively. The shrinkage of lake area mainly driven by climate change and permafrost thaw was regulated by the geometric features of lakes, with lakes having longer shorelines or larger areas demonstrating more pronounced responses to permafrost thaw. These findings emphasize the regulatory effect of lake geometric features on the response of lake area changes to climate change and permafrost thaw, indicating the need to consider lake geometric features when predicting lake evolution in the context of Arctic permafrost thaw.

Spatial networks of urban heat islands (UHIs) within urban agglomerations are characterized by a non-trivial topology. They critically influence regional climate dynamics and promote coordinated development. For the first time at the national scale, this study constructs a 15-year comparative framework for 19 major Chinese urban agglomerations by systematically integrating multi-source thermal remote sensing data, morphological spatial pattern analysis (MPSA), resistance-based circuit modeling, and complex network robustness theory. A combination of the Simplified Urban Extent algorithm, MSPA, circuit theory, and simulation of network robustness not only identified and quantified UHI connectivity patterns but also evaluated their systemic vulnerabilities under both random and targeted node-removal scenarios. This enabled resilience-oriented optimization. The results show that the area of summer UHI patches in the 19 agglomerations expanded significantly from 6.79 x 104 km2 in 2005 to 1.52 x 105 km2 in 2020. Correspondingly, the total length of the spatial networks increased from 5.79 x 104 to 9.98 x 104 km, with the density rising by 0.01 km.km-2, reflecting both large-scale spatial expansion and enhanced inter-patch connectivity. Targeted attack simulations further revealed that removal of structurally critical nodes-identified via multi-metric network centrality assessment-reduced network resilience by 35-60 % more than did random disruptions, highlighting the roles played by key spatial hubs in terms of maintaining UHI network stability. These findings bridge landscape ecology and complex network science, offering a transferable resilience-oriented framework for optimization of urban climate networks and heat risk mitigation in rapidly urbanizing regions.

Urban thermal environments at the local scale exhibit pronounced spatial heterogeneity and temporal accumulation effects, posing significant challenges for effective heat mitigation. Among passive cooling strategies, shading directly reduces incoming solar radiation. However, its cumulative temporal effects remain insufficiently quantified. A high-resolution dynamic downscaling framework coupling WRF-UCM and ENVI-met was employed to simulate hourly urban microclimate conditions. To quantify the cooling contribution of cumulative shading duration on land surface temperature (LST), a two-resistance mechanism (TRM) model was incorporated for process-based attribution. Results showed that the coupled framework achieves high simulation accuracy and physical consistency when validated against observational data. Shading-induced cooling effect intensifies with cumulative duration, with three consecutive hours of shade consistently reducing LST by up to 13.23 °C during summer sunlight hours. Under the same shading duration, areas with higher impervious surface coverage and limited pervious patches exhibited stronger cooling effects. In some cases, shorter-duration shading over highly impervious surfaces produced greater cooling than longer-duration shading over fully pervious surfaces, highlighting the nonlinear interaction between shading duration and surface permeability. Mechanistic attribution further revealed that reductions in net radiation (Rn*) are the dominant driver of shading-induced cooling effects, while surface resistance (rs) plays a secondary and context-dependent role. These findings provide new mechanistic insights into the spatiotemporal regulation of urban microclimates and support the design of adaptive heat mitigation strategies in urban planning.

In the context of climate change and ecological degradation, enhancing cropland productivity in Northeast China is essential for ensuring national food security. This study adopted an integrated framework combining the optimal parameter-based geographical detector (OPGD) and SHapley Additive exPlanations (SHAP) to identify key drivers of average and total cropland productivity at the county level from 2001 to 2020. Growing-season-based cropland Net Primary Productivity (NPP) was estimated using the CASA model to represent cropland productivity. Results indicated that natural and ecological factors significantly dominated the spatial variation of cropland productivity, with their interactions amplified through dual-factor or nonlinear enhancements. Various machine learning models were fine-tuned and compared, and optimal models were selected for subsequent SHAP analysis. The findings revealed that erosion intensity exhibited the most significant impact on cropland productivity, whereas the effect of precipitation shifted from negative to positive, with a clear threshold of around 400 mm—matching the boundary between China’s semi-arid and semi-humid regions. Low-elevation plains (<300 m) and gentle slopes (<0.5°) predominately promoted total cropland productivity. Interactions between erosion and fertilizer intensity highlighted the need for moderate fertilization to prevent ecological degradation in severely eroded counties. These findings provide scientific support for targeted cropland management aimed at achieving sustainable agriculture in Northeast China.

Urbanization-driven land use and cover change exacerbates urban warming and amplifies the effects of global climate change, particularly in interconnected urban agglomerations. This study utilized the Weather Research and Forecasting (WRF) model coupled with an Urban Canopy Model (UCM) to investigate how urbanization and intercity interactions influence urban warming during the summer season in the Beijing-Tianjin-Hebei (BTH) urban agglomeration, with a specific emphasis on whether the warming effect of urbanization exacerbates under heatwave scenarios. The results showed that from 2010 to 2020, the expansion of urban areas in the BTH urban agglomeration from 2259.81 to 3964.80 km2 led to a 0.406 degrees C increase in 2-m temperatures (T2) in typical summer. Heatwave further exacerbated the warming effects in the urban agglomeration, with T2 rising by 0.648 degrees C during heatwave periods compared to 0.308 degrees C during non-heatwave periods. Major cities like Beijing and Tianjin significantly contributed to the warming of the entire urban agglomeration and other specific cities during heatwave periods. These findings underscore the need for collaborative urban planning to mitigate heatrelated risks and manage compounded effects of urbanization and heatwave.

The temperature of the Qinghai-Tibet Plateau (QTP) has rapidly increased under global change, accelerating the process of lacustrine eutrophication. Chlorophyll-a (Chla) has always been a key indicator of lacustrine phytoplankton biomass and eutrophication. Satellite images have incomparable advantages in estimating the long-term and large-scale variation of Chla under extremely harsh climate conditions. In order to effectively manage lakes and work toward Sustainable Development Goals, this research made use of Landsat surface reflectance data from 1986 to 2023, which were collected and processed on the Google Earth Engine platform, to remotely estimate Chla in lakes on the QTP. Among the 1,069 lakes studied in the QTP, 281 and 166 lakes showed significant increases and decreases in Chla (P < 0.05), respectively. Correlations between Chla and multiple environmental factors were analyzed (analyzed in particular nitrogen deposition, large livestock, and sheep factors, besides conventional factors), aiming to bring new inspiration to water environment management. Utilizing variance decomposition and multiple general linear model regression, we quantitatively analyzed the contribution of various environmental factors. This study creatively set up the 3 change scenarios of large livestock, sheep, and fertilizer into general-linear-model-based equations to forecast yearly variations in Chla in 10 typical lakes from 2024 to 2100. The concentration of Chla in most lakes exhibited a marked rise due to the yearly increase in large livestock, sheep, and fertilizer. We suggest reducing human activities in lakes facing high environmental pressures and redirecting some environmental pollution pressure to lake basins with more space for self-ecological system regulation in the future.

Microscale numerical simulation models are widely applied to explore potential factors and adaptive strategies for localized high temperatures in urban surface or near-surface environments. However, few studies address the limited availability of meteorological input data and the use of multiple meteorological outputs to investigate the mechanisms between factors as a theoretical verification for simulation. This study used the WRF-UCM model outputs in Tianjin, China, as the basic background meteorological field for microclimate simulation and compared the improvement in simulation accuracy of LES-based scheme (PALM-4U) and RANS-based software (ENVI-met) in predicting pedestrian-level air temperature and relative humidity during the downscaling simulation. Subsequently, attribution analysis of land surface temperature imbalance is performed using the tworesistance model (TRM) based on surface and atmospheric simulation outputs which also aids in verifying the applicability of the one-way downscaling simulation framework. It is found that the WRF-UCM-RANS framework exhibits superior overall performance, reducing the error in 2-m height relative humidity by approximately 50 % at the same location compared to mesoscale results. The attribution results indicate that localized high temperature on impervious surfaces within urban neighborhood are primarily driven by surface resistance (rs) during the daytime heating process and ground heat storage (G) during nighttime cooling. However, surface resistance (rs) remains the dominant driving factor influencing land surface temperature throughout both daytime and nighttime. The framework reduces the challenge of obtaining initial meteorological data and provides technical support for expanding microclimate research to multi-site simulations and future scenario predictions in complex urban environment.

Industrial particulate matter (PM) emissions significantly contribute to air pollution and soil heavy metal contamination via deposition. In this study, the spatial distribution and sectoral variations in particulate matter-permitting emissions (PMpe) from heavy metal-emitting enterprises across China are analysed. We employed piecewise structural equation modelling to quantify the relative influences of geographic location, socioeconomic development, and climate conditions. The results revealed strong spatial agglomeration of enterprises, which exhibited a distinct "dense east, sparse west" pattern. High-emission clusters for PMpe (>0.200) were predominantly located in northern regions, specifically Northwest China (44.01 ×10 ³ t/a) and North China (31.86 ×10 ³ t/a). Significant sectoral differences were detected, with nonferrous metal smelting resulting in the highest total emissions (101.51 ×103 t/a) and average PMpe of each pollutant outlet (7.27 t/a). Geographical location had the strongest direct effect on PMpe, with the total effect value reaching 0.253. Climate conditions demonstrated notable positive direct impacts in North China, with the total effect value reaching 0.589. Socioeconomic factors showed weaker influences overall, although they exhibited significant positive direct effects in Central and Northeast China. Regionalization based on dominant influencing factors underscores the need for tailored pollution control strategies to increase mitigation precision. These findings provide crucial scientific support for the establishment of regionally differentiated prevention and control strategies for heavy metal emissions.

Northeast China is one of China's most important rice production bases, contributing about one-fifth of the country's rice production. In recent years, several agricultural policies have been implemented in Northeast China to adjust crop structures, driven by economic and ecological benefits. Timely monitoring of the changed pattern of rice cultivation is a prerequisite for policy assessment. Current paddy rice mapping methods are experiencing uncertainties due to confusion with wetlands and are highly parameter-dependent. To these, in this study, we developed a paddy rice mapping framework that integrates automatically generated training samples, time series features from key cultivation stages, and a deep learning model to improve paddy rice identification accuracy in Northeast China, which is a typical rice-wetland coexisting area. We produced 10 m paddy rice maps for 2019-2023 in Northeast China and examined their changes under agricultural policy implementation. The resultant paddy rice maps have high accuracies, with overall accuracies >0.97, producer's accuracies >0.94, user's accuracies >0.93, and F1 scores of >0.95, respectively. Our proposed mapping method effectively identified small patches of paddy rice and reduced confusion with wetlands. Paddy rice areas in Northeast China estimated in this study continued to decrease annually from 71.1 x 10(3) km(2 )in 2019 to 56.4 x 10(3) km(2) in 2023. Hot spots of rice conversion to other crops were found in the Sanjiang Plain, mainly due to the Soybean Revitalization Plan. The rice cultivation expansion was mainly found in the Songnen Plain, resulting from policies on Rehabilitation and Utilization of Saline Soils. Observed changes in paddy rice plantation emphasize the importance and necessity of timely and continuous crop cultivation monitoring under the influence of agricultural policy adjustment.

The imbalance between the supply and demand of cropland multifunction has emerged as a key challenge for the sustainable use of cropland in high-density population areas globally. However, current research mainly focuses on supply-side mechanisms and lacks a systematic analysis of the spatial relationships and factors influencing the supply-demand matching of cropland multifunction, thereby constraining decision-making for managing multifunctional mismatches. To solve these problems, this study constructs an integrated framework of "supplydemand matching - process response - zoning management," combining the eXtreme gradient boosting, Shapley additive exPlanations for interpretability, to analyze the spatial heterogeneity and influencing factors of cropland multifunction supply-demand relationships in China. Based on the clustering of self-organizing maps, spatially adaptive governance strategies are proposed. Results show that: (1) Cropland multifunctional supply-demand presents significant spatial mismatches, with most regions showing supply surpluses; 47.9 %, 54.9 %, and 75.9 % of areas exceed demand in production, ecological, and landscape-cultural functions, respectively, while 52.4 % of areas exhibit deficits in social security functions. (2) Factors influencing supply-demand matching vary markedly among different cropland functions, with population size and economic development accounting for 21 %-39 % and showing pronounced nonlinear effects. (3) Eight cropland functional clusters are identified, supporting targeted governance strategies such as cross-regional resource coordination, ecological compensation, and urban-rural cultural integration. This study highlights the importance of considering spatial relationships and influencing factors in spatial policy formulation, offering recommendations for optimizing cropland resource allocation and promoting sustainable management.

The COVID-19 pandemic has profoundly impacted the tourism industry, particularly red tourism. This study investigates the spatiotemporal characteristics and dynamics of population flow in red tourism counties during the pre-pandemic and post-pandemic periods, aiming to provide valuable insights for sustainable red tourism planning and policy formulation. This study covers 180 red tourism counties across 20 provinces in China, utilizing mobile signaling data from May and October of 2018, 2020, 2022, and 2023. The results show that, in 2023, the total population inflow to red tourism counties reached 4448.85 × 104, with a notable spatial disparity, mainly concentrated in northern and central regions, such as Beijing, Linyi, and Liu’an. The inflow was primarily from eastern and central provinces, such as Guangdong, Hebei, and Henan, with these areas showing substantially higher visitation than western and northeastern regions. These inflows were strongly influenced by geographic proximity and transportation accessibility, with a significant increase during holiday periods reflecting the role of tourism policies in shaping mobility patterns. From 2018 to 2023, population dynamics fluctuated significantly due to the pandemic, with a strong recovery by 2023, surpassing pre-pandemic levels by 1332.26 × 104. The recovery rates varied regionally, with areas such as Inner Mongolia and Qinghai showing substantial growth, while provinces such as Beijing and Shanxi showed slower recovery. These findings underscore the enduring appeal of red tourism and highlight the effectiveness of targeted policy interventions. However, regional disparities in recovery rates suggest that focused efforts are needed to ensure balanced and sustainable red tourism development.

Stability serves as one of the key dimensions of food security and agricultural production systems, particularly in the context of climate change and increasing climate variabilities. To investigate how agricultural inputs, climate fluctuations, and their interactions affect the temporal stability of grain production, this study compiled multisource provincial-level data in China from 1991 to 2020 at 5-year intervals and calculated time-detrended stability indices for both grain production and yield. The results indicated that precipitation fluctuation during the crop-growing seasons and natural disasters significantly reduced both grain production and yield stability, while the effect of temperature fluctuation was less substantial. The negative impacts of nitrogen fertilizer application on grain stability highlighted the importance of considering and addressing ecological degradation; furthermore, interaction terms reveal that it underscores the vulnerability of grain production and yield stability to climate variability, particularly to precipitation fluctuation. By contrast, irrigation benefits grain stability by satisfying water demands and demonstrates a mitigating effect on risks from precipitation fluctuations. In addition, higher farmers' incomes strengthen their incentives for agricultural engagements, underscoring the critical role of agricultural subsidies and policy support. These findings provide scientific support for targeted management of agricultural inputs in response to climate fluctuations and for ensuring food security through a sustainable agriculture approach.

As a key region for national food security, Northeast China (NEC) is under growing pressure to balance agricultural productivity and water availability amid global climate change and rising food demand. These challenges underscore the need for efficient, spatially targeted irrigation strategies to optimize water use and sustain crop production. In this study, we apply the Global Agro-Ecological Zones (GAEZ) model to assess the impacts of climate change and irrigation on yield potential dynamics across NEC from 2000 to 2020. We further conduct multi-scenario analysis to explore the outcomes of increasing irrigated area proportion by 10 %, 30 %, and 50 %, evaluating their effects on yield gap closure and climate change mitigation. Our results show an average annual increase in yield potential of 56.36 kg.ha-1.a-1 across the region. Climate change caused a 1.23 % loss in multiyear total yield potential, with 68.28 % of these losses occurring in rainfed areas, while 80.54 % of yield gains were observed in irrigated areas. Except for rice, which experienced moderate gains (49.31 kg.ha-1 annually), other major crops-particularly maize and soybeans-were negatively affected by climate trends. Irrigation offset nearly 4.81 times the total climate-induced yield losses, although its positive impact has declined over time. Among the scenarios, a 30 % increase in irrigated area proportion demonstrated the greatest potential, particularly for maize. Under this scenario, yield gaps could be closed and climate-induced losses fully compensated in 16.32 % and 17.82 % of NEC croplands, respectively, primarily in the southern Songnen Plain, Liao River Plain and Greater Khingan Mountains Region. These findings provide a scientific basis for optimizing irrigation strategies to ensure food security and promote sustainable water resource management.

The coordination and stability of the regional economy and ecological environment is the premise and foundation for realizing the synergistic development of the Beijing-Tianjin-Hebei region, so it is necessary to systematically analyze and study it. In this paper, based on analyzing the current situation and evolution of the characteristics of regional economic (RE) and ecological environmental quality (EEQ) indicators in the Beijing-Tianjin-Hebei urban agglomerations(BTH), we construct an evaluation system for coupling and harmonization of RE and EEQ, and based on the coupled coordination degree (CCD) model, conduct a systematic analysis of the spatial-temporal coupled coordination of the RE and EEQ of the BTH in the period of 2000-2020. The results show that (1) in 2020, the GDP of the Poverty-stricken counties around the Beijing-Tianjin (PSC-BT) counted for 4.57% of the total BTH, and the area of high-quality ecosystems, the net primary productivity(NPP), the amount of soil conservation, water conservation, and sand fixation accounted for more than 40% of the total BTH, respectively. (2) From 2000 to 2020, the GDP growth of the PSC-BT accounts for 4.45% of the total growth of BTH, the reduction of the area of high-quality ecosystems accounts for 21.04% of the total reduction of BTH, and the growth of NPP, soil conservation, water conservation, and sand fixation account for about 40% of the total growth of BTH, respectively. (3) From 2000 to 2020, the CCD of BTH as a whole showed an upward trend, but the gap between the regions gradually expanded, and the development type changed from RE lagging to EEQ lagging, in which the coupling coordination grade of the PSC-BT was in the dissonance stage for a long time, and Southern Hebei was lower than northern Hebei, which had always been in the type of RE lagging, and the level of economic development had to be improved. Correctly recognizing the spatio-temporal coupling status of RE and EEQ, and adopting appropriate development policies have important guiding significance for realizing the synergistic development of BTH.

Urban thermal environment (UTE) issue has reduced the quality of life for residents, and urban green spaces (UGs) can effectively improve the UTE. However, at a fine scale, the cooling capacity of UGs with different structures and the diurnal variation still require in-depth study. This article, based on land use, vegetation cover, ground elevation, anthropogenic heat emission, and meteorological data, utilizes the UrbClim model to simulate high-resolution hourly air temperature (Ta) in Tianjin's urban area during summer high-temperature days. It analyzes the structural characteristics of UGs at a fine scale and reveals the diurnal cooling effect variations among UGs with various structures. The results indicate that the Urban Heat Island (UHI) within Tianjin's urban area consists of a primary heat island and several smaller heat islands scattered around it. The northern part experiences higher Ta, while the southeastern part is cooler, with the highest daytime Ta ranging from 30.7 to 34.2 degrees C and the lowest nighttime Ta ranging from 26.0 to 31.7 degrees C. The structure of UGs significantly influences the spatial pattern and fluctuation of Ta. UGs with high coverage show a diminished cooling effect at night and can reduce the extent of daily Ta fluctuations, particularly when the greenery exceeds 80%, where the effect is most pronounced. The Ta stability of patchy and aggregated UGs is stronger than that of linear and scattered UGs. Vegetation types have different regulatory effects on diurnal Ta; during the day, trees have a more significant cooling effect than grasslands, while at night, this cooling effect is attenuated. Furthermore, the cooling benefits were amplified during extreme heat days. At the hottest time of the day, UGs with a high percentage of area, patchy and aggregated shape, and tree vegetation type have the best cooling effect, while at the coldest time of the night, these UGs slow down the dissipation of heat accumulated during the day and have a significant attenuation of the cooling effect. Our findings underscore the intricate relationships between the cooling effects and landscape structure of UGs, thereby aiding in the formulation of urban planning strategies to maximize the cooling benefits.