Against the backdrop of global climate change and rising uncertainty, the resilience of urban agglomerations, as the core of regional development, is critical to national security. This study investigates the spatiotemporal evolution and driving mechanisms of urban resilience in 136 counties of the Shandong Peninsula urban agglomeration from 2010 to 2022. We constructed an urban resilience evaluation system and employed methods such as the entropy-weighted TOPSIS model, kernel density estimation. The main findings are: (1) Urban resilience showed a fluctuating upward trend, with a moderating growth rate. Development across dimensions is imbalanced; the economic dimension is the primary contributor, while the ecological dimension has recently shown rapid growth. (2) Spatially, resilience exhibits a gradient differentiation pattern of “higher in the east and north, and lower in the west and south”, forming four high-resilience county clusters with strong spillover effects. The level of coupling and coordination among the various subsystems of urban resilience has steadily improved. Positive spatial autocorrelation is significant, with clusters being predominantly “high-high” (expanding) and “low-low” (stable). (3) The dynamic evolution of urban resilience shows path dependence and club convergence. The transitions of urban resilience are stable and gradual, with few leapfrog breakthroughs. A significant spatial spillover effect exists: “high-level neighborhoods promote upward mobility in resilience levels, while low-level neighborhoods reinforce development lock-in”. (4) Spatiotemporal differentiation is driven by multiple factors. Economic vitality is dominant, while the natural environment, urban scale and people’s living standards are key supports. Factor interactions are predominantly enhancing, especially those involving economic vitality. The effects of different factors varied substantially across time and space. These findings provide empirical support for optimizing urban planning, helping the western counties of the study area break free from the “low-low” lock-in and promoting coordinated regional resilience improvement.

Study Region: Yellow River Delta (YRD), China. Study Focus: This study systematically integrated synthetic aperture radar (SAR) data, extensometer deformation data, groundwater level data, satellite altimetry data, and sea level rise (SLR) projections to comprehensively explore the effects of regional subsidence on relative sea level rise (RSLR) and coastal flood inundation in the YRD. Furthermore, this study analyzed the deformation characteristics of soil layers at different depths and identified the main subsiding layers. New Hydrological Insights for the Region:

This study examines how the construction and operation of urban sports centers influence carbon emissions from a micro perspective to inform greener urban space planning. Using a quasi-experimental analytical framework, it evaluates stage-specific carbon effects across multiple cities. The results show that carbon emissions rise significantly during the construction phase, while the operational phase produces no overall average effect, although some cities experience notable emission reductions. The direction of impacts is consistent across cities but varies in magnitude. Economic development and population size exhibit a V-shaped moderating influence on carbon outcomes across stages, whereas land uses associated with carbon sources and sinks display opposite moderating mechanisms. These findings suggest that differentiated carbon-reduction strategies and context-sensitive site selection are essential, offering practical guidance for urban planners and policymakers working toward long-term carbon mitigation goals.

Urban–rural integration (URI) represents a pivotal pathway to realizing sustainable development within urban–rural spatial systems. It is of paramount importance in addressing the challenge of reconciling ecological conservation with high-quality development in the Yellow River Basin. Leveraging panel data from 78 cities in the Yellow River Basin spanning the years 2006–2023, this research constructs an evaluation index system that encompasses five dimensions: population, economy, society, ecology, and space. Through the comprehensive application of kernel density estimation, exploratory spatiotemporal data analysis, and panel quantile regression models, a systematic analysis of the spatiotemporal evolution patterns and transition mechanisms of URI is conducted. The results disclose that URI in the Yellow River Basin demonstrates a trend of “overall enhancement with regional disparities”. From 2006 to 2023, the URI of the basin witnessed an average annual growth rate of 2.86%. Spatially, it presented distinct features: high-level agglomeration in the lower reaches, accelerating-growth path dependency accompanied by internal divergence in the middle reaches, and balanced yet low-level development in the upper reaches. The local spatial evolution of URI follows a pattern characterized as “predominant stability and limited transitions”. In detail, high-level regions sustain their advantages, low-level regions encounter obstacles in achieving breakthroughs, and the spillover effects between adjacent regions remain relatively restricted. The driving mechanisms exhibit significant “phase-spatial” dual heterogeneity, with four distinct patterns identified. In light of these findings, policy recommendations are put forward, including the establishment of a multi-scale, coordinated spatial governance system.

Identifying and zoning semiconductor industry agglomerations are foundational and pioneering tasks for the national planning layout and high-quality development of this industry. This study, based on headquarters-branch investment relationship data of 15 757 Chinese semiconductor enterprises in 2022, uses complex network graph segmentation techniques such as location quotient, minimum spanning tree, and modularity to construct a semiconductor industry network, covering 256 prefecture-level cities in China (excluding Hong Kong, Macao, and Taiwan of China). The study reveals that: 1) the spatial distribution pattern of China’s semiconductor industry is predominantly concentrated, with a non-uniform development trend of higher levels in the east and south compared to the west and north; 2) the overall outwards investment network exhibits a geographical distribution resembling a diamond structure and rhombus pattern. Local investment agglomeration areas differ significantly between the east and west, with the east having a dual-core or multi-core traction type and the west displaying a single-core traction type; 3) among the six major agglomeration areas, the Yangtze River Delta (YRD) has the highest level of regional integration. The Pearl River Delta (PRD) shows strong internal connections but significant polarisation on its east bank of the PRD. The Beijing-Tianjin-Hebei (BTH) urban agglomeration exhibits a gradient-driven dual-core structure, whereas the Shandong Peninsula urban agglomeration relying on the eastern Shandong metropolitan area and western Shandong metropolitan area forms horizontal connections. Dual-core growth pole agglomeration patterns are observed in the Guanzhong Plain urban agglomeration and Chengdu-Chongqing urban agglomeration; 4) ultimately, the spatial agglomeration area of China’s semiconductor industry can be divided into four primary zones (the southern zone of China, the eastern zone of China, the northern zone of China, the western zone of China) and 20 secondary zones. The results can be directly applied to national and regional industrial planning, particularly for defining development priorities, optimizing interregional division of labor, and reducing spatial fragmentation in China’s semiconductor industry.

Accurate assessment of ecosystem service value (ESV) is crucial for sustainable environmental management, especially in regions with high ecological sensitivity and significant socioeconomic importance. This study focuses on the Yellow River Basin and integrates the land-use transition matrix, equivalent factor method, ecosystem service trade-off and synergy analysis, and the optimized parameters geographical detector to analyze the spatiotemporal evolution and driving mechanisms of ESV from 2000 to 2023. The results show that (1) cropland and grassland are the main land-use types in the Yellow River Basin, and during rapid urbanization, the expansion of construction land mainly comes at the expense of cropland and grassland. (2) the total ESV in the basin has steadily increased, with grassland as the primary contributor among land types; regulating services, particularly hydrological regulation, are the core ecosystem services in terms of supply, regulation, support, and cultural functions. (3) High-ESV areas in the eastern and central parts of the basin have expanded over time, exhibiting a spatial pattern of higher values in the west and lower in the east, distributed mainly along the river, with clustering effects gradually weakening. (4) Ecosystem services demonstrated predominantly synergistic relationships, suggesting potential for integrated ecosystem management. (5) Population density, DEM, mean annual temperature, and slope are the dominant factors influencing spatial variation in ESV, with the combined effects of topography and climate significantly enhancing the explanation of ESV heterogeneity. This study deepens the understanding of the evolutionary mechanisms of ecosystem services in the Yellow River Basin and provides scientific support and decision-making references for regional ecological compensation mechanisms, optimized land resource allocation, and watershed ecosystem management.

Study region: Beijing Plain (BJP), China. Study focus: The rapid land subsidence in BJP has been alleviated since the South-to-North Water Diversion Project. Groundwater level (GWL) is recovering with more precipitation from climate change. The land deformation pattern is evolving into a coexistence of subsidence-rebound. Hence, Sentinel-1A and InSAR were used to investigate surface deformation in 2016-2022, and a new Transfer Function Analysis (TFA) framework was proposed by integrating deformation, precipitation, wells, and hydrogeological data. This study quantified the response characteristics among precipitation, GWL, and deformation according to TFA, aiming to explore the differential response mechanisms of subsidence-rebound to GWL affected by monsoon precipitation. The maximum rebound was estimated. New hydrological insights for the region: Compared to 2011-2015, the area with a subsidence rate of over 60 mm/yr in 2016-2022 has decreased by 37 %. Local areas have experienced a rebound, the area with a rebound rate of over 5 mm/yr is 67.2 km2. The seasonal response between precipitation and GWL exists throughout the plain, while the seasonal response between GWL and deformation is only consistent in the northwest. The aquifer schematic models suggest that the differential deformation response is related to lithology and residual deformation. In the southeast, the aquifer head is still below the adjacent aquitard head, with a larger residual deformation disturbing the seasonal response caused by precipitation. A longer delay between GWL recovery and surface rebound was observed in the aquifer with thicker clay layers, with over 77 % of the compaction being irreversible.

Green technology innovation (GTI) plays a crucial role in promoting regional sustainable development, yet its mechanisms for mitigating air pollution remain insufficiently explored. This study investigates the spatio-temporal dynamics and spatial correlations of GTI and air pollution across China's major river basins, with a particular focus on differentiating the roles of GTI quantity and quality. Using the extended STIRPAT and spatial Durbin models, we examine nonlinear relationships, basin-specific heterogeneity, and spatial spillover effects. Results show that GTI quantity followed a core-periphery diffusion pattern, while GTI quality exhibited fluctuating trends with recent stabilization. An inverted "U" shaped relationship was observed between both GTI dimensions and PM2.5 and PM10 concentrations, locally and in neighboring areas. Notably, inflection points for GTI quantity lagged behind those for quality, and post-inflection pollution-reduction effects differed across basins-being stronger in the Yangtze River Basin for PM2.5, but in the Yellow River Basin for PM10. These findings highlight the differentiated mechanisms through which GTI affects air quality, offering valuable insights for targeted innovation policies and regional coordination in sustainable governance.

Greenhouse gas emissions are a leading cause of global warming, posing significant threats to both the natural environment and the sustainable development of global economies and societies. Environmental regulations have been crucial in reducing these emissions and improving ecological conditions. This study presents the first theoretical analysis of the mechanisms through which formal and informal environmental regulations influence carbon intensity. A panel data model was constructed to empirically test and analyze the impact of these regulations on carbon intensity across 118 countries. The findings reveal that both formal and informal environmental regulations exerted a reduced influence on global carbon emission intensity, affirming their significance in promoting energy conservation and emission reduction. Moreover, a synergistic effect was observed between the two types of regulations, indicating that their coordinated enforcement could further mitigate carbon emission intensity. Regional analysis revealed that formal environmental regulation exhibited a dampening effect on carbon emission intensity in both high- and low-carbon countries. However, the moderating impact of informal environmental regulation was found to be markedly stronger in low-carbon countries compared to their high-carbon counterparts. The synergy between these two forms of regulation had a more pronounced influence on the carbon emission intensity of high-carbon countries, underscoring their potent emission reduction effect when implemented in tandem. The insights gained from this study can aid policymakers in developing effective environmental policies aimed at realizing energy conservation and emission reduction goals, thereby contributing to the mitigation of global warming and the promotion of sustainable development.

Leveraging multi-source remote sensing datasets and dynamic groundwater monitoring well observations, this study explores the multiscale spatiotemporal linkages of groundwater storage changes and land deformation in North China Plain (NCP) after the South-to-North Water Diversion Project (SNWDP). Firstly, we employed Gravity Recovery and Climate Experiment (GRACE) and interferometric synthetic aperture radar (InSAR) technology to estimate groundwater storage (GWS) and land deformation. Secondly and significantly, we proposed a novel GRACE statistical downscaling algorithm that integrates a weight allocation strategy and GWS estimation applied with InSAR technology. Finally, the downscaled results were employed to analyze spatial differences in land deformation across typical ground fissure areas. The results indicate that (1) between 2018 and 2021, groundwater storage in the NCP exhibited a declining trend, with an average reduction of −3.81 ± 0.53 km3/a and a maximum land deformation rate of −177 mm/a; (2) the downscaled groundwater storage anomalies (GWSA) showed high correlation with in situ measurements (R = 0.75, RMSE = 2.91 cm); and (3) in the Shunyi fissure area, groundwater storage on the northern side increased continuously, with a maximum growth rate of 28 mm/a, resulting in surface uplift exceeding 70 mm.

Cross-regional green technology innovation transfer (GTIT) plays a vital role in mitigating climate change and improving environmental quality. However, its spatio-temporal dynamics and pollution reduction effects remain insufficiently understood. Using green patent transfer data during 2001–2021, this study examined intercity GTIT in China, identified transfer types, constructed a network structure, and explored its air pollution reduction mechanisms across regions and time periods. The results show a significant increase in green patent flows, with large regional disparities—transfers in the eastern region far exceed those in the central and western regions. A rhombus-shaped GTIT network has formed, centered on the Beijing-Tianjin-Hebei, Yangtze River Delta, Pearl River Delta, and Chengdu-Chongqing urban agglomerations, with Beijing emerging as the national hub. Moreover, intercity GTIT significantly reduces air pollution, with stronger pollution reduction effects observed in the eastern region compared to the central and western regions. The implementation of key policies in 2012 and 2017 further enhanced the pollution reduction effects. The pollution reduction effect also shows significant spatial spillover. Additionally, GTIT indirectly influences air pollution through industrial upgrading and the enhancement of local green innovation capabilities, which in turn promote cleaner production, lower energy consumption, and a shift toward more sustainable, low-emission industries. This study not only advances the understanding of GTIT in China but also offers insights and policy references for other countries aiming to harness green technology for sustainable development and environmental governance.

The Yellow River Basin (YRB) is a significant economic development region in China; however, it faces the challenge of underdeveloped economic levels, which impacts the sustainable development of the national economy. This study constructs an index system for high-quality economic development (HQED) based on five development concepts. The CRITIC method was utilized to comprehensively evaluate 78 prefecture-level cities in the YRB from 2000 to 2022. Techniques such as the Dagum Gini coefficient, exploratory spatial data analysis, Markov chain analysis, and the obstacle degree model were employed to investigate the temporal and spatial evolution of HQED levels and their associated obstacles in the YRB. The findings indicate a positive temporal trend in the HQED index, with increasing intra-group differences and overlapping issues among regions, while inter-group differences are decreasing. Nevertheless, the primary contradiction in the YRB continues to arise from inter-group disparities. Spatially, the development regions are predominantly centered around provincial capitals, exhibiting a pronounced “fault line” phenomenon and characteristic “spatial proximity.” In terms of evolutionary trends, the likelihood of each region maintaining its current state is relatively high; however, regions with higher-quality neighborhoods demonstrate a lower probability of stability and a greater likelihood of upward mobility. The positive impacts of high-quality neighborhoods outweigh the negative effects associated with low-quality areas. In terms of obstacles, factors such as sharing and coordination hinder progress in HQED in the YRB, with challenges related to coordination, innovation, and openness intensifying in recent years.

Urban–rural integration (URI) is important for achieving rural revitalization and sustainable development. Currently, there is a lack of research on URI in prefecture-level cities in the Yellow River Basin Urban Agglomerations (YRBUAs), and the spatial relationship between URI and economic development is not clear. This paper evaluates the URI index of the YRBUA from 2010 to 2022, and applies research methods such as an exploratory data analysis, spatial variation function, coupling coordination degree, and gray correlation analysis to explore the spatial relationship between URI and economic development. The study found the following: (1) The integration level is highest in the Shandong Peninsula Urban Agglomeration and lowest in the Ningxia Along the Yellow River Urban Agglomeration. The rank structure of the URI index within the city cluster is dominated by a single core. (2) The URI index roughly shows the spatial distribution characteristics of high in the east and low in the west. (3) The level of URI and economic development are spatially positively correlated. The high-value agglomeration areas of both are mainly distributed in Shandong Peninsula. (4) The high-value areas of coupling coordination and gray correlation degree are distributed in Shandong Peninsula Urban Agglomeration.

In recent years, population aging has started to profoundly affect the sustainable development of human society. Compared to developed countries, China faces a more pronounced challenge of “aging before affluence”. Based on data from China’s 5–7th national censuses, we selected relevant indicators such as the population aging rate and aging population growth rate to explore the multi-scale spatiotemporal evolution of population aging in the Bohai Rim Region during 2000–2020. On this basis, we classified the types of regional population aging. Representative indicators from two dimensions—population and socio-economic factors—were selected to analyze the driving factors of population aging using the Geodetector method. The results show that the degree of population aging exhibited a deepening trend in the Bohai Rim Region during 2000–2020, with its spatial clustering characteristics becoming increasingly pronounced. However, spatial clustering at the district and county scales was weaker than that at the prefectural scale. Overall, population aging exhibits a pattern whereby the eastern regions, centered on the Liaodong Peninsula and Jiaodong Peninsula, experience deeper levels of aging compared to the western regions. The growth rate of the aging population followed a declining trend across administrative divisions, in the order of urban districts, county-level cities, counties, and autonomous counties. At the prefectural, district, and county scales, demographic and socio-economic factors demonstrated significant influences, with population factors showing higher q-values than socio-economic factors. There are significant spillover effects of demographic and socio-economic factors on population aging in the BRR, with fertility, education, and urbanization being key drivers. Policy recommendations should focus on addressing regional disparities, with aging cities needing expanded care services and regions affected by out-migration requiring community-based care and better resource integration.

It is urgent for the wastewater treatment sector to respond to global climate change. Although studies related to the water–energy–carbon (WEC) nexus have been widely conducted, the application of the coupling coordination indicator is still limited in the wastewater treatment sector. This study fills such a research gap by linking water footprint (WF), energy footprint (EF), and carbon footprint (CF) together and testing these indicators in 140 wastewater treatment plants (WWTPs) in Shandong province, China. Both the EF and CF of these WWTPs were calculated by conducting hybrid life cycle assessments, while WF was calculated by using a WF method. The results show that gray WF generated from 1 m3 of wastewater ranged from 9.58 to 12.90 m3, while EF generated from 1 m3 of wastewater ranged from 9.42 × 10−2 to 0.22 kg oil eq and CF generated from 1 m3 of wastewater ranged from 0.58 to 1.27 kg CO2 eq. Also, the total WF, EF, and CF of these WWTPs in Shandong were 4.26 × 1010 m3, 5.32 × 108 kg oil, and 3.35 × 109 CO2 eq in 2021, respectively. Key factors contributing to the overall greenhouse gas (GHG) emissions were the on-site GHG emissions and off-site electricity-based GHG emissions. Meanwhile, total nitrogen was the dominant contributor to the gray WF. In addition, the coupling coordination indicators of WF, EF, and CF ranged from 0.7571 to 0.9293. Finally, this study proposed several policy recommendations to improve the overall sustainability of this wastewater treatment sector by considering local realities, including adopting multi-dimensional indicators, decarbonizing current electricity grids, promoting the utilization of renewable energy, and initiating various capacity building efforts.

Accurate long-term estimation of forest aboveground biomass (AGB) is essential for understanding carbon dynamics and assessing the impacts of climate change and human disturbance. However, generating high-resolution, continuous AGB time series remains challenging due to data limitations and methodological constraints. In this study, we present a 21-year (2000-2020) reconstruction of forest AGB in China's Great Xing'an Mountains by integrating multi-temporal MODIS imagery with spaceborne LiDAR data from the GEDI L4B product using the AutoGluon stacking ensemble learning algorithm. All models achieved root mean square errors (RMSE) below 25 Mg/ha, with weighted ensemble model yielding superior performance (R-2 = 0.83, RMSE = 13.99 Mg/ha, rRMSE = 14.38 %). Trend analysis based on Sen's slope and the Mann-Kendall test revealed a significant regional increase in AGB, with 83.36 % of forest area exhibiting upward trends, while 16.64 % showed declines. Fire disturbance emerged as a primary driver of localized AGB loss, particularly in the northern and eastern subregions. From 2000 to 2020, average forest AGB increased by 14.67 Mg/ha, and total biomass rose by 0.53 Pg. These results demonstrate the potential of combining GEDI and MODIS data with machine learning for large-scale, long-term forest biomass monitoring, offering valuable support for carbon accounting, ecological assessment, and forest management in cold-temperate ecosystems.

Enhancing market integration levels is crucial for advancing sustainable regional collaborative development and achieving ecological protection and high-quality development goals within the Yellow River Basin, fostering a balance between economic efficiency, social equity, and environmental resilience. This study analyzed the retail price data of goods from prefecture-level cities in the Yellow River Basin from 2010 to 2022, employing the relative price method to measure the market integration index. Additionally, it examined the temporal and spatial evolution patterns and driving factors using the Dagum Gini coefficient and panel regression models. The results indicate the following. (1) The market integration index of the Yellow River Basin shows a fluctuating upward trend, with an average annual growth rate of 9.8%. The spatial pattern generally reflects a situation where the east is relatively high and the west is relatively low, as well as the south being higher than the north. (2) Regional disparities are gradually diminishing, with the overall Gini coefficient decreasing from 0.153 to 0.104. However, internal differences within the downstream and midstream areas have become prominent, and contribution rate analysis reveals that super-variable density has replaced between-group disparities as the primary source. (3) Upgrading the industrial structure and enhancing the level of economic development are the core driving forces, while financial support and digital infrastructure significantly accelerate the integration process. Conversely, the level of openness exhibits a phase-specific negative impact. We propose policy emphasizing the need to strengthen development in the upper reach of the Yellow River Basin, further improve interregional collaborative innovation mechanisms, and enhance cross-regional coordination among multicenter network nodes.

With rapid urban expansion and frequent natural disasters, green infrastructure (GI) networks are highly susceptible to disturbances and impacts. Improving the resilience of GI networks is important for maintaining species migration, improving ecological efficiency, and realizing sustainable development. Therefore, an effective GI network resilience measure must be constructed to measure it more accurately. There is a lack of current research that utilizes dynamic simulation methods to measure GI network resilience. This paper selects Jinan, China as the study area, uses morphological spatial pattern analysis and landscape connectivity analysis methods to select GI sources, and uses minimum cumulative resistance and the gravity model to construct the GI network. We construct the evaluation index of GI network resilience. Using this index to evaluate the GI network resilience to changes under various disturbance scenarios. The results show that the network resilience level is good. Networks exhibit different levels of resilience under random and intentional attack conditions. The nodes in the top 10% of the node degree value are critical to maintaining the operation of the network system. This paper expected that the network resilience evaluation method could be enriched and provide references for decision-makers to formulate sustainable urban development strategies.

In the context of the “ecological priority and green development” strategy, examining the co-evolution between the tourism economy and the efficiency of urban green development can offer both theoretical insights and quantitative foundations to support ecological preservation and high-quality development in China’s Yellow River Basin. This research utilized approaches such as the Haken model and the geographically and temporally weighted regression model to investigate the spatiotemporal patterns, synergistic characteristics, and driving factors of the tourism economy and urban green development efficiency within the Yellow River Basin. The findings reveal the following: (1) Regional disparities in the tourism economy are progressively widening, whereas the efficiency of green development tends to decline. Furthermore, both the tourism economy and urban green development efficiency display “high-high clustering” and “low-low clustering” spatially. (2) The synergistic evolution of the two systems displays spatial characteristics of transitioning from polarization to trickle-down effects. (3) Natural factors such as topography and vegetation coverage, as well as human economic factors like industrial structure and the degree of openness, positively promote the synergy. However, elements such as temperature, precipitation, economic development level, and openness to innovation have a certain inhibitory effect on the synergistic evolution.

Based on the analysis of the spatiotemporal evolution characteristics of producer services agglomeration and urban green development efficiency in China, this study measures the influence and spatial spillover effects of producer services agglomeration on urban green development. Research results reveal that the specialization agglomeration level of producer services has undergone a dynamic decline process, demonstrating spatial characteristics where the east exhibits higher levels than the west, and the north surpasses the south. In contrast, the diversification agglomeration level of producer services has demonstrated a consistent upward trajectory, characterized by a spatial distribution that is broadly scattered but concentrated on a smaller scale. Regarding China's urban green development, its efficiency shows a dynamic upward trend and displays characteristics of agglomeration and contiguous development in space. Overall, both modes of producer services agglomeration have beneficial diffusion impacts on urban ecological advancement. Furthermore, the impact of this agglomeration on the efficiency of green development notably varies across regions and industries.