Search Results (2,540)

Improving land green utilization efficiency (LGUE) is essential for achieving sustainable development in China. This study investigates the spatiotemporal evolution and localized driving mechanisms of land green utilization efficiency across 127 cities in six major Chinese urban agglomerations from 2011 to 2023. Previous research frequently overlooks the spatial non-stationarity and structural interactions within regional land governance. To address this theoretical gap, a comprehensive multiscale framework is employed. This framework integrates the Super-SBM model, Dagum Gini decomposition, Spatial Markov chains, and Multiscale Geographically Weighted Regression. The empirical results reveal an overall upward efficiency trajectory alongside persistent spatial inequalities. A pronounced scale-efficiency inversion is observed between developed eastern coastal and developing central-western inland regions. Furthermore, spatial interaction analysis identifies a significant backwash effect. This mechanism constrains the upward mobility of peripheral cities adjacent to high-efficiency core nodes. The multiscale regression demonstrates substantial spatial heterogeneity in the effects of key driving factors. Elements such as industrial structure and financial development exhibit highly localized associations dependent on regional institutional contexts. These findings bridge macroeconomic growth models with micro-environmental governance. The study provides critical empirical evidence for shifting from uniform administrative management to spatially targeted regional policy frameworks. Full article

Xinjiang experiences frequent dust storms, posing significant challenges to regional ecological security and public health. Based on the China High-resolution and High-quality Near-surface Air Pollutants (CHAP) dataset and ground monitoring data, this paper adopts the Potential Source Contribution Function (PSCF) to analyze the spatiotemporal characteristics of atmospheric particulate matter across Xinjiang and typical cities and to identify potential source regions and contribution intensities. The results show that (1) PM_2.5 and PM₁₀ concentrations are elevated in southern Xinjiang but reduced in the north, and particulate pollution in most areas has generally decreased. (2) Northern Xinjiang cities have high PM_2.5 in winter, while southern Xinjiang cities keep persistently high PM₁₀ levels. (3) The PM_2.5/PM₁₀ ratio is above 0.35 in northern cities, where pollution is dominated by fine particles affected mainly by human activities; southern Xinjiang is dominated by coarse particles from natural sources. (4) Particulate matter in Urumqi mainly comes from the northern Tianshan Mountains, with winter WPSCF over 0.9. Pollutants in Kashgar originate from both long-distance cross-border dust transmission and local emissions. These findings are of great significance for the sustainable development of Xinjiang and urban agglomerations along the Belt and Road. Full article

Against the backdrop of rapid urbanization, urban land-resource use faces the dual challenge of improving efficiency while maintaining ecological sustainability. Enhancing urban land green use efficiency contributes to the achievement of the United Nations Sustainable Development Goals, particularly SDG 11 and SDG 15. As an emerging governance approach for urban green infrastructure, the National Sponge City Policy (NSCP) aims to address urban waterlogging through nature-based solutions while improving land multifunctionality and ecological carrying capacity. Based on city-level panel data from 2005 to 2022, this study employs a difference-in-differences (DID) approach to identify the policy effect of the NSCP on ULGUE and further examines three transmission channels: innovation effects, infrastructure-support effects, and population-agglomeration effects. The novelty of this study lies in integrating the NSCP into the analytical framework of urban land green use efficiency, extending previous research that mainly focused on waterlogging control, water-resource management, and ecological benefits, and further developing a “policy intervention-factor reallocation-ULGUE improvement” mechanism pathway. The empirical results show that the NSCP significantly improves land green use efficiency in pilot areas, and this conclusion remains valid across multiple robustness checks. The mechanism analysis indicates that strengthened green innovation capacity, improved green infrastructure, and population agglomeration are key channels through which the policy effect is realized. Heterogeneity analysis further reveals that the policy effect varies across regions, dominant industrial structures, and industrial-base types. Overall, the NSCP promotes green spatial governance and efficient resource utilization, providing important institutional experience for coordinating ecological protection and urban development. Full article

Using panel data for 135 Chinese prefecture-level cities from 2007 to 2023, this study investigates the impact of new digital infrastructure on green finance development. The new digital infrastructure indicator is constructed based on the proportion of relevant keywords appearing in government work reports, while the green finance index is reconstructed using the entropy-weighting method across seven dimensions. The estimation results indicate that new digital infrastructure exerts a significant positive effect on green finance development. This conclusion remains robust after a series of robustness checks, including alternative variable measurements, winsorization treatment, and instrumental-variable estimation. Mechanism analysis reveals that industrial structure upgrading, particularly the advancement of industrial structure, serves as an important transmission channel. Further heterogeneity analysis shows that the promoting effect is more pronounced in cities with larger economic scale, those located outside major urban agglomerations, and cities with higher levels of financial resource aggregation. These findings provide empirical evidence for the role of digital infrastructure in fostering green finance and facilitating sustainable regional development. Full article

For old industrial urban agglomerations, low-carbon planning requires emission information at a finer spatial scale, but county-level energy statistics are often incomplete. This study focuses on the Central-Southern Liaoning Urban Agglomeration, a typical heavy-industrial region in Northeast China. County-level energy-related carbon emissions for 73 units from 2005 to 2024 are reconstructed by combining socioeconomic panel data with harmonized DMSP-OLS-like nighttime light data. On this basis, global and local spatial autocorrelation, Moran scatterplots, Markov and spatial Markov transition matrices, and a spatial STIRPAT-based Spatial Durbin Model are used to examine the spatial pattern, transition process, and driving factors of emissions. The results show that emissions continued to increase during the study period, although the growth rate became slower and no clear regional peak was observed. Moran’s I rose from 0.627 in 2005 to 0.675 in 2024, which means that county-level emissions became more spatially clustered. The traditional Markov matrix shows strong state persistence, with diagonal probabilities ranging from 0.8793 to 0.9852. The spatial Markov results further suggest that counties surrounded by high-emission neighbors face greater pressure to move upward. In the SDM results, the spatial autoregressive coefficient is significant at the 1% level, with rho = 0.537. GDPPC and POP show negative direct effects, SEC increases local emissions but has a negative indirect effect, and PE is positively related to local emissions. Spatially, high-emission counties are mainly distributed around Shenyang, Anshan, Liaoyang, Dalian, and other industrial cores, while eastern ecological counties remain at relatively low emission levels. These findings provide county-scale evidence for differentiated low-carbon governance in old industrial regions. Full article

Investigating the spatiotemporal dynamics of urban heat islands and their responses to urban forest (UF) landscape patterns is crucial for mitigating urban thermal stress. However, the nonlinear influence and conditional constraints of UF landscape composition and configuration on the warming effects across varying urbanization gradients remain inadequately understood. By integrating land use/cover data, MODIS-derived land surface temperature (LST), and meteorological datasets, this study employed the XGBoost-SHAP model to quantify the nonlinear and interaction effects of UF landscape patterns on developed and developing urban regions of the Pearl River Delta. The results indicate that (1) spatial clustering patterns of warming varied significantly between the two regions, with substantial seasonal heterogeneities (p < 0.05). Summer exhibited the most intense warming, characterized by more rapid temperature increase in developed areas than in developing regions. (2) Relative to UF landscape metrics, the proportion of impervious surfaces, precipitation, and temperature exerted greater influence on regional warming. Coverage area, fragmentation, and connectivity of UFs emerged as the primary landscape drivers modulating warming. In developed areas, spatial configuration metrics exerted greater influence on LST than compositional metrics. (3) The responses of LST to diverse UF landscape patterns are characterized by nonlinearity and pronounced threshold effects. These landscape thresholds vary by season, revealing critical tipping points for warming suppression; however, this regulatory effect is highly context-dependent. Specifically, under high percentages of impervious surface, the warming-suppression capacity of UFs intensifies with increasing percentage of UF area or core. Our findings highlight the necessity of strategic UF planning and forest fragmentation mitigation for developing effective climate resilience strategies. These results provide a foundation for adaptive planning tailored to specific urbanization stages and the implementation of targeted UF cooling strategies. Full article

This study reframes street-level sustainable urban renewal as a coordination problem between street vitality and relative low-carbon performance, rather than treating vibrant activity and carbon-pressure reduction as separate planning objectives. Its main contribution is an integrated street-level diagnostic framework that combines multidimensional vitality measurement, township-constrained carbon-emission reference estimation, vitality–carbon mismatch identification, and interpretable nonlinear mechanism analysis within unified street analytical units. Although previous studies have substantially advanced the measurement of street vitality and urban carbon emissions, these two strands of research have often developed separately. As a result, limited evidence is available on whether high-vitality streets also perform well in low-carbon terms, where vitality–carbon mismatches emerge, and which built-environment conditions are associated with more coordinated outcomes. Taking the five central districts of Chengdu, China, as a case, this study integrates multi-source activity, mobility, built-environment, and emission-related data. Street vitality is measured through activity agglomeration, temporal continuity, functional support, and external connectivity, while relative low-carbon performance is derived from the reverse normalization of length-normalized carbon-emission intensity based on a township-constrained street-level emission reference estimate. The results show that street vitality and low-carbon performance are spatially uneven and frequently mismatched, as high activity does not automatically translate into stronger low-carbon performance, and lower-carbon pressure does not necessarily indicate a vibrant urban environment. More coordinated streets are associated with context-specific combinations of functional organization, transport operation, built form, street-interface quality, and ecological background. Nonlinear diagnostic results further suggest that coordination is favored by moderate, balanced, and locally adapted built-environment conditions rather than by the simple maximization of individual indicators. These findings shift the discussion from whether vitality and low-carbon performance are desirable in isolation to how they can be jointly diagnosed and improved in street-level urban renewal. Full article

Baiyangdian Lake, the largest freshwater lake in North China, plays a critical role in the ecological security of the Beijing–Tianjin–Hebei urban agglomeration. This study conducted systematic monitoring of Baiyangdian Lake from April 2023 to November 2024. Utilizing the Trophic State Index (TSI) and principal component analysis (PCA), we elucidated the impact mechanisms of extreme precipitation events on the water quality of shallow lakes. The results indicate that: (1) During the study period, Baiyangdian Lake exhibited moderate to severe eutrophication. The average total nitrogen (TN) concentration was 2.13 mg/L, exceeding the Class V threshold of the national surface water quality standard. The average total phosphorus (TP) concentration was 0.05 mg/L, far surpassing the recognized eutrophication threshold for freshwater lakes. (2) The average TSI was 49.6 ± 4.0, indicating the lake is in a transitional state from mesotrophy to eutrophy, with 64% of sampling sites classified as eutrophic. Nitrogen was identified as the primary limiting nutrient. (3) The 2023 extreme precipitation event exerted a significant three-phase impact on water quality: “dilution–legacy–restoration”. A clear dilution effect was observed from the pre-flood to the flood period (TN decreased from 1.52 to 1.04 mg/L). A pronounced legacy effect emerged post-flood, with the TN concentration sharply increasing to 4.22 mg/L in September 2023, the highest value recorded during the study. (4) PCA identified two major pollution sources: agricultural non-point source pollution (PC2, contribution: 25.4%) and domestic sewage/livestock farming (PC1, contribution: 27.6%). Correlation analysis further revealed that the flood event significantly altered the intrinsic relationships among parameters like nitrogen and phosphorus, reinforcing the dominance of agricultural non-point source pollution. (5) Source analysis suggests that external inputs are the primary contributors, while the internal loading from sediments is relatively limited. This study enhances the understanding of how shallow lakes respond to extreme climatic events and provides a scientific basis for lake management in the Beijing–Tianjin–Hebei region. Full article

Climate change and rapid urbanization increasingly threaten water security in large river basins, yet existing assessments often fail to capture the multi-scale interactions between hydroclimatic extremes and human activities. To address this gap, we developed an integrated framework combining risk assessment, multi-method driver diagnosis (Geodetector, Multi-Scale Geographically Weighted Regression (MGWR), and Structural Equation Modeling (SEM)), and Zoned Management. Using a landscape-derived Ecological Risk Index (ERI) as a proxy indicator of runoff and non-point source potential, based on established empirical linkages between landscape metrics and hydrological processes, we applied the framework to three major urban agglomerations in the Yangtze River Basin from 2000 to 2020. Our results reveal three distinct risk mechanisms: in the Chengdu–Chongqing area (CYUA), a 165.8% increase in impervious surfaces drives altered runoff; in the Middle Reaches (MRC), the q-value of the Standardized Precipitation Index (SPI) rose from 0.017 in 2000 to 0.146 in 2020, corresponding to a 759% relative increase. Although the absolute q-value of SPI remains moderate at around 0.15, its rapid rise suggests increasing hydrological sensitivity of the MRC’s river–lake system to precipitation extremes; in the Yangtze River Delta (YRD), socioeconomic activities exert overriding pressure. Based on these diagnostics, we propose tailored strategies for water environment management, adaptive planning, and disaster mitigation. This framework offers a scientific basis for differentiated water governance in large river basins facing coupled anthropogenic and hydroclimatic pressures. Full article

Enhancing mariculture’s green total factor productivity (GTFP) is essential to balance industrial growth with ecology, safeguard global food security, and meet UN Sustainable Development Goal 14 amid mounting marine stress. As a global leading mariculture producer, China provides a typical research sample. This study constructs a mariculture GTFP measurement index system, estimates GTFP in China’s coastal provinces via the global Super-SBM model, identifies root causes of efficiency loss, and explores influencing factors and spatial spillover effects using a spatial econometric model. The results show that the overall mariculture GTFP of China’s coastal provinces exhibits a fluctuating upward trend with significant regional heterogeneity, specifically presenting a distribution pattern of “the highest in the South China Sea Region, followed by the East China Sea Region, and the lowest in the Yellow Sea and Bohai Sea Region”. Meanwhile, mariculture GTFP shows significant positive spatial autocorrelation, with distinct High-High and Low-Low agglomeration characteristics. Excessive resource consumption and undesirable output discharge are the core drivers of efficiency loss. For direct effects, industrial scale, industrial structure, fishermen’s income, transportation accessibility, internet development, technology adoption, and environmental regulation significantly boost local GTFP, while fishery disasters exert a significant negative impact. For spatial spillovers, industrial scale, industrial structure, and internet development show significant positive effects, while fishermen’s income and urbanization present negative effects. Based on these findings, this study proposes targeted multi-stakeholder optimization paths, providing decision support for China’s mariculture green development and replicable experience for global coastal countries. Full article

This study aims to reveal the spatial-temporal evolution rule and driving mechanism of cropland multifunctionality in major grain-producing areas. Taking Henan Province as the research case, we establish a comprehensive evaluation index system covering production, living, ecological and cultural functions based on multi-source datasets spanning 2013–2022. It adopts the entropy weight method, spatial analysis and geographical detector (GeoDetector) model to analyze the spatial-temporal differentiation characteristics and influencing mechanism of cropland multifunctionality systematically. The results show that the overall level of cropland multifunctionality in Henan Province rose from 2013 to 2022. Its spatial pattern presents a feature of high in the south and low in the north, with obvious agglomeration in southern Henan. The production function is high in the east and low in the west with a stable pattern. The living, ecological and cultural functions all show a distribution of high in the south and low in the north, with prominent regional differences. Factor detection results indicate that average slope, population density and average annual temperature are the core driving factors. The overall influence of natural factors is stronger than that of socio-economic factors. Interaction detection shows that all factors produce a strengthening effect, mainly in the form of nonlinear enhancement effects. Based on this, the research has proposed targeted and differentiated strategies for the management of cultivated land. Specifically, southern Henan should consolidate its inherent multifunctional advantages and strengthen the coordinated development of production, ecological and cultural functions. Northern and western Henan needs to mitigate terrain and climatic constraints, optimize agricultural infrastructure, and improve overall cropland service capacity. Eastern plain areas should further stabilize grain production function while balancing ecological protection. Central urban agglomerations should coordinate urban expansion and cropland protection to restrain multifunctional degradation. Full article

The urban–rural income gap remains a central issue in the income distribution of developing countries, constraining regional coordination and social equity. Although stable economic development is essential for narrowing this gap, the distributional consequences of local economic growth management have received insufficient attention, especially from the perspective of urban spatial structure. Drawing on the urban bias theory and spatial economics, this study uses panel data from 41 prefecture-level cities in the Yangtze River Delta region of China during 2007–2023 and applies a two-way fixed effects model to examine the effect of economic growth target pressure on the urban–rural income gap and the mediating role of urban spatial structure. The results show that economic growth target pressure significantly widens the urban–rural income gap, with an estimated increase of approximately 0.001–0.002 units in the Theil index. Mediation analysis further indicates that target pressure promotes a more monocentric urban spatial structure, which partially mediates the effect. The results also show evident temporal and regional heterogeneity. These findings suggest that growth-oriented local governance may reshape income distribution through spatial organization, offering empirical evidence for optimizing local economic management and urban spatial structure to promote coordinated urban–rural development. Full article

Against the backdrop of escalating global climate risks, reconciling economic expansion with ecological sustainability has emerged as a core challenge for urban sustainable development worldwide. This study leverages China’s Climate-Adaptive City Pilot Policy as a quasi-natural experiment and employs staggered difference-in-differences (DID) estimation on panel data covering 280 Chinese cities from 2006 to 2024 to evaluate the policy’s causal effect on urban green total factor productivity (GTFP). The empirical results yield three key findings. First, climate-adaptive urban construction delivers a significant improvement in GTFP, with a pronounced time-lagged effect: the policy exerts no statistically significant impact in the short term but generates substantial positive outcomes in the long run, verifying the dynamic implications of the strong Porter hypothesis. Second, mechanism analysis reveals two valid transmission channels through which the policy boosts GTFP, namely the expansion of firm entry (particularly the entry of non-polluting enterprises) and the agglomeration of high-skilled talents. Notably, the talent agglomeration channel is only effective in cities with advanced economic development. Dynamic tests further confirm that both firm entry and talent agglomeration responses to the policy follow consistent short-term insignificant and long-term significant patterns. Third, heterogeneous analysis demonstrates that the policy’s green growth dividends are more prominent in southern cities, non-resource-based cities, and national transportation hub cities. This study extends the existing literature on the green efficiency effects of climate adaptation policies and provides empirical evidence and differentiated policy insights for optimizing urban green transformation governance in the new era. Full article

Rapid urbanization exerts profound pressure on urban biodiversity, yet long-term assessments integrating multi-source remote sensing data remain scarce. Objective: Focusing on the Hangzhou Bay Urban Agglomeration, a rapidly developing region in China’s Yangtze River Delta, this study aims to construct a remote sensing-based Biodiversity Index (BI) and analyze its spatiotemporal evolution and underlying drivers. Six Essential Biodiversity Variables derived from satellite observations (2000–2024) were integrated using Principal Component Analysis. Spatial autocorrelation and Geodetector models were then applied to examine BI dynamics and driving factors. The regional BI declined gradually from 0.80 in 2000 to 0.72 in 2024, with the rate of decline slowing after 2020 and a partial recovery observed in Zhoushan. Marked inter-city heterogeneity exists: Huzhou retains the highest and most stable BI due to extensive forest cover, whereas Jiaxing exhibits the lowest BI and the most pronounced decline, driven by rapid expansion of construction land. Land use/cover (LULC) and fractional vegetation cover (FVC) emerge as the dominant drivers (average q-values of 0.196 and 0.208, respectively), and their interaction explains over 46% of the spatial variance in BI. Road density shows a consistently increasing influence over time. This study demonstrates the utility of remote sensing-based frameworks for monitoring urban biodiversity dynamics and provides actionable insights for evidence-based land use planning and ecological restoration. Full article

Using panel data from Chinese cities spanning 2010–2023 and leveraging the natural experiment provided by the establishment of the National Big Data Comprehensive Pilot Zone (NBDPZ), we employed the difference-in-differences (DID) method alongside double machine learning (DML) to systematically examine how these policies influence urban economic resilience efficiency. The empirical results demonstrate that the NBDPZ significantly enhances urban economic resilience efficiency. This finding is robust under parallel trend and placebo tests, confirming that the improvement is a policy-driven causal effect. Mechanism analysis reveals that the policy enhances urban economic resilience efficiency primarily by promoting the upgrading and rationalization of industrial structure to consolidate the micro-foundation of sustainable economic transformation; increasing innovation output to facilitate the sustainable accumulation of knowledge capital; and enhancing urban entrepreneurial activity to inject sustainable endogenous vitality into the economic system. Heterogeneity analysis indicates that the positive effects are more pronounced in eastern and western regions, second-tier cities, and cities with lower industrial agglomeration, better digital infrastructure, and stronger legal and regulatory environments. The study’s findings offer both theoretical support and practical guidance for refining the policy framework of the NBDPZ policy and promoting sustainable urban economic development. Full article