Search Results (1,180)

This study investigates the spatial patterns and drivers of carbon emissions across China’s three major urban agglomerations—Beijing–Tianjin–Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD)—from 2011 to 2020. A sequential analytical framework was employed to examine emission inequality, spatial dependence, dynamic transitions, and multi-scale drivers. Specifically, the Gini and Theil indices were used to quantify and decompose regional disparities. Spatial clustering patterns and heterogeneity were then identified through global and local Moran’s I analysis. Following this, spatial Markov chains modeled state transitions and neighborhood spillover effects. Finally, the Spatial Durbin Model (SDM) was applied to distinguish between the direct and indirect effects of key socioeconomic drivers. The findings reveal that disparities in emissions are largely driven by factors within each region. In BTH, heavy industrial lock-in accounts for 47.1% of the within-group inequality. By contrast, the YRD and PRD show noticeable convergence, achieved through industrial synergy and technological restructuring, respectively. The mechanisms of spatial spillover also differ across regions. In the YRD, emissions exhibit strong clustering tied to geographic proximity, with Moran’s I consistently above 0.6. In BTH, policy linkages play a more central role in shaping emission patterns. Meanwhile, in the PRD, widespread technological diffusion weakens the conventional distance-decay effect. The influence of key drivers varies notably among the urban agglomerations. Economic growth has the strongest scale effect in the PRD, reflected by a coefficient of 0.556. Industrial transformation significantly lowers emissions in the YRD, with a coefficient of −0.115. Technology investment reduces emissions in BTH (−0.124) and the PRD (−0.076), but is associated with a slight rebound in the YRD (0.037). Overall, these results highlight the persistent path dependence and distinct spatial interdependencies of carbon emissions in each region. This underscores the need for tailored mitigation strategies that are coordinated across administrative boundaries. Full article

Emergency supplies policies are a key component of regional risk governance, yet their design coherence has received limited systematic examination. Focusing on the Yangtze River Delta (YRD), this study conducts a design-oriented evaluation of emergency supplies policy design by integrating policy text mining with the Policy Modeling Consistency (PMC) index model. Based on a corpus of 212 emergency supplies–related policy documents, the study first examines the structural features and thematic emphases of the regional policy system and constructs a PMC-based evaluation framework within a mission–structure–mechanism perspective. On this basis, 16 provincial- and municipal-level policies issued between 2019 and 2023 are identified as core, system-defining policy texts and subjected to in-depth PMC evaluation. The results indicate that the evaluated core emergency supplies policies exhibit an overall “good” level of design coherence. Mission-oriented dimensions, including normative orientation and policy objectives, are generally well articulated, whereas mechanism-oriented dimensions—particularly linkage response and allocation arrangements—are specified less consistently. Observed interjurisdictional differences reflect institutional roles and governance traditions rather than variations in administrative capacity. By shifting analytical attention from implementation outcomes to design-stage coherence in core policy texts, this study provides a structured diagnostic approach for assessing emergency supplies policy design and offers insights for strengthening regional coordination and institutional resilience. Full article

China’s National Eco-Industrial Parks (NEIPs) represent a significant policy intervention designed to achieve the synergistic reduction in pollution and carbon emissions. While previous studies have examined the impacts of NEIPs on pollution and carbon emissions in isolation, research on their synergistic reduction is still limited. This study constructs a Carbon-Pollution Co-Reduction Index (CPCRI) with weights determined by the entropy weight method (EWM) to capture the joint performance of emission intensities. By applying a staggered difference-in-differences (SDID) model to city-level panel data from the Yangtze River Delta between 2003 and 2021, the study finds that NEIPs significantly improve the CPCRI of cities where NEIPs are located by 2.30 percentage points. This positive effect exhibits a time lag, becoming statistically significant three years after establishment and strengthening thereafter. Mechanism analyses indicate that the synergistic reductions are driven by technological innovation and reduced energy intensity, while heterogeneity analyses reveal that the policy effect is more pronounced in economically developed provinces and larger cities but has diminished in recent years. Then, a coupling coordination degree (CCD) is integrated to construct a new index to capture both joint performance and synergy between reductions. These findings provide robust empirical support for NEIPs as a practical policy tool to achieve sustainable industrial transformation in the Yangtze River Delta. Full article

Accurately grasping the relationship between science and technology finance efficiency (STFE) and carbon emission efficiency (CEE), and further exploring their interaction and synergistic development within the network structure are of great significance for promoting regional coordinated development, economic growth, and environmental issues. This article uses the super-efficient SBM model to measure the STFE and CEE in 30 provinces of China from 2011 to 2020, and innovatively introduces the Multi-Layer Network (MN) method to explore the characteristics of their network structure, synergistic evolution, and influencing factors. The results show that (1) the evolution of the MN structure is the result of synergistic development, which mainly forms the network pattern of the Beijing–Tianjin–Hebei, the Yangtze River Delta, and the Qinghai–Gansu region with “triple-core, multi-zone”. (2) The STFE network plays a leading role in the MN structure by influencing the CEE network structure. (3) The layers of MN are connected in a disassortative way, while the network similarity is gradually increasing. (4) The number of communities of the MN is decreasing, and the agglomeration of the community structure is gradually increasing. (5) The performance of the MN structure has better robustness than the single-layer network under different strategies and different node retention levels of destruction. (6) The economic development level, government support rate, and industrial structure upgrading are the core factors affecting the value of weighted degree and closeness centrality, while betweenness centrality is mainly affected by the urbanization level and foreign direct investment level. Full article

This study proposes a novel framework for allocating CO₂ emission rights within the Yangtze River Delta (YRD) urban agglomeration, tackling the inter-city CO₂ transmission dynamics frequently neglected in conventional allocation models. Current emission allocation methods fail to capture the spatial spillover effects of CO₂ emissions driven by atmospheric transport, resulting in potential inequities. Leveraging the WRF model to simulate carbon emissions across 27 cities, we develop a two-stage network Data Envelopment Analysis (DEA) model that integrates both emission generation and governance capacities. Our findings highlight significant inter-city CO₂ transmission, with the wind direction and speed playing a pivotal role in emissions spread. In contrast to traditional models, our approach considers the regional interdependence of emissions, enhancing both fairness and efficiency in the allocation process. The results indicate that cities with stronger governance systems, including green technology investments and effective air quality management, are rewarded with higher carbon allowances. Moreover, our model demonstrates that policies prioritizing environmental governance over raw emission levels can foster long-term sustainability. This work provides a comprehensive methodology for achieving a balanced allocation of emission rights that integrates economic growth, environmental management, and equity considerations within complex urban agglomerations. Full article

Based on micro-level job seeker data from 41 cities in China’s Yangtze River Delta, this study employs threshold regression to examine how inter-city distance influences talent mobility. The results reveal that distance exerts a negative impact on mobility intention and moderates the relationship between a destination’s economic level and mobility. Notably, significant threshold effects are identified at 164.1 km and 271.5 km, delineating three spatial regimes. Short-distance flows (<164.1 km) show the highest intensity, driven by strong economic incentives and high mobility. In contrast, medium-distance flows (164.1–271.5 km) prove least attractive due to offsetting effects, while long-distance flows (>271.5 km) rebound slightly as talent selectively targets major economic hubs, with distance exhibiting only weak inhibition. Crucially, these nonlinear patterns remain robust after addressing endogeneity concerns via the 2SLS method, substituting spatial distance with temporal distance, and controlling for housing prices and cultural factors. Heterogeneity analysis further indicates that individuals with bachelor’s degrees, those above age 30, and talent in labor-intensive industries exhibit greater sensitivity to distance. Conversely, knowledge-intensive sectors and top-tier economic cities demonstrate broader spatial tolerance and stronger cross-regional attraction capabilities. These findings provide a quantitative basis for developing differentiated regional talent policies. Full article

Against the backdrop of national policies promoting coordinated regional development and ecological civilization construction, the contradiction between ecological security and environmental carrying capacity in the Yangtze River Delta urban agglomeration has become increasingly prominent. However, the interaction mechanisms between these two systems remain insufficiently explored. This study constructs a comprehensive evaluation indicator system for ecological security and environmental carrying capacity in the Yangtze River Delta. A double exponential function is employed to examine the intensity of interaction pressure and reveal their nonlinear relationship. The coupling coordination model is applied to assess coordinated development trends, while a vector autoregression (VAR) model is used to identify the dynamic response relationships among system variables. The results indicate that the overall levels of both systems have improved, with core areas maintaining a leading position and southeastern, northeastern, and western regions remaining in a catching-up stage, accompanied by low-level convergence. Regional coordination exhibits a positive temporal evolution from imbalance to coordination, while its spatial pattern evolves from core dominance toward multi-regional convergence. Significant regional heterogeneity is observed in shock responses, with peripheral cities facing stronger industrial restructuring pressures showing greater ecological volatility. Overall, the dynamic interaction between ecological security and environmental carrying capacity demonstrates a stage-specific transition from mutual constraint to mutual promotion. This study provides empirical support for ecological restoration and regional sustainable development policymaking. Full article

Rapid urbanization has profoundly impacted regional ecosystem services. However, most current studies have not paid enough attention to the implicit quality-of-life dimensions of urbanization, and few studies have been published on the dynamic interactions between urbanization and the evolution of ecosystem services. This study investigated the temporal and spatial dynamics of urbanization and ecosystem services value (ESV) in the Yangtze River Delta (YRD) region from 2010 to 2020 and their correlation. We conceptualized and measured the level of urbanization in two dimensions: Urbanization I (population, economy, and landscape) and Urbanization II (public services, education and spiritual life, and habitation environment construction). ESV was quantitatively evaluated by the equivalent factor method. The global and local spatial autocorrelation analysis was used to reveal the influence of urbanization dynamic evolution on ESV change. The results show the following: (1) the level of Urbanization I rose steadily, while the level of Urbanization II, though starting from a lower base, grew at a significantly faster rate, especially after 2017; (2) total ESV declined, with the largest decline in regulating services; (3) a significant negative spatial correlation was found between urbanization and ESV, with Urbanization I exerting a greater negative impact than Urbanization II; (4) spatially, “high-low” clusters (high urbanization, low neighboring ESV) dominate in the eastern coastal areas, while “low-high” clusters dominate in the western inland areas. The findings are of great significance for regional sustainable development and can provide a reference for other rapidly urbanizing regions in the world. Full article

Traditional villages are integral to the broader context of global socio-economic transition. This study developed a resilience evaluation model centred on built-environment indicators. This model integrates the community, economy, ecology, and culture dimensions. Clarifying the typology and key driving factors of traditional village built environment resilience can effectively activate the inherent potential of villages. The study provides a holistic approach to identifying traditional village built environment resilience types and analysing the key influencing factors. Utilising a method combining the SOM-K-means clustering model and the interpretable XGBoost-SHAP model, the study provides a holistic analytical framework for identifying traditional village built environment resilience types and quantifying the nonlinear action characteristics of various indicators across different types. Taking the Yangtze River Delta (YRD) region as an example, the study demonstrates that traditional villages can be categorised into six potential resilience types, with differentiated key indicator combinations across these types. Furthermore, the nonlinear action characteristics and operational thresholds of the same key indicator differ significantly across various traditional village types. For instance, at medium-to-high threshold levels, the accessibility of cultural buildings contributes significantly to the sustainability of culture–service-driven villages but, conversely, becomes a detriment in ecology-cultural composite archetypes. Similarly, in industry–creative driven villages, once the density of cultural and creative spaces reaches a specific threshold, it exerts a significant positive effect on traditional village development and stabilises into a sustained positive state. However, in ecology–agriculture–organisation-driven villages, exceeding a certain threshold in the density of cultural and creative spaces has a significant negative influence. The results provide an analytical framework for the resilience typology and influencing factors of traditional village built environments, consequently offering a scientific basis for formulating refined, differentiated policies for traditional villages. Full article

Spatial heterogeneity in economic benefits of water use provides crucial evidence for the evaluation of water diversion projects and the spatial equilibrium of water resource allocation. Using city-level data from 2017 on the sectoral water use and value added in 334 Chinese cities, we estimated the economic benefits of water use in the agricultural, industrial, and service sectors using the allocation coefficient method. We then revealed the spatial heterogeneity combining an exploratory spatial data analysis (ESDA) method. For the agricultural sector, the high economic benefit of water use regions are primarily concentrated on both sides of the “Hu Huanyong Line”; regions with high economic benefit of industrial water use are mainly found in the North China Plain, the middle and lower Huanghe River basin, the Yangtze River Delta, the Pearl River Delta, Chongqing and Chengdu, and the economic benefit of service water use is higher in the north than in the south. ESDA provides significant evidence for the analysis of spatial heterogeneity with regard to the economic benefits of water use in China. Based on the fundamental distribution of water resources and the spatial heterogeneity in the economic benefits of water use, potential water diversion areas can be preliminarily identified. The Haihe River Basin in the North China Plain and some areas in the southeast coastal region are potential receiving areas, and the eastern regions of Southwest China with abundant water resources and lower elevations, along with the middle and lower reaches of the Yangtze River are potential source areas. Further research about marginal benefits and water use costs, along with dynamic updates, is required for water resource allocation of China. Full article

Accurate nitrogen dioxide (NO₂) forecasting is crucial for proactive emission control and issuing public health warnings. This study provides the first evaluation of the China Meteorological Administration’s (CMA) operational CUACE/Haze-Fog V3.0 numerical prediction system, assessing its daily NO₂ forecast accuracy against independent satellite measurements and in situ observations. We compare model forecasts with TROPOspheric Monitoring Instrument (TROPOMI) satellite column data and observations from 1677 Chinese ground monitoring stations, focusing on four key regions: the Yangtze River Delta, Pearl River Delta, Beijing–Tianjin–Hebei, and Urumqi. An optimal spatial resolution of 0.15° × 0.15° was determined for TROPOMI data processing. The results indicate a strong seasonal dependency in model performance. The model systematically underestimates NO₂ concentrations in winter but performs significantly better in summer. This systematic bias is confirmed by a Normalized Mean Bias (NMB) consistently below −20% in northern regions during the winter. In the Beijing–Tianjin–Hebei region, the Root Mean Square Error (RMSE) reached 3.57 × 10¹⁵ molec/cm² (vs. TROPOMI) and 1.09 × 10¹⁵ molec/cm³ (vs. ground stations) in winter, decreasing to 0.95 and 0.91, respectively, in summer. Critically, this winter bias pertains to pollution magnitude rather than temporal correlation; the model captures pollution trends but underestimates peak severity. Our study reveals a ‘vertical decoupling’ in the operational forecasting system. While the model utilizes surface data assimilation to correct surface pollutants, this study demonstrates that these corrections fail to propagate vertically to the total NO₂ column during winter stable boundary layer conditions. This finding has broader implications for chemical transport models (CTMs): relying solely on surface data assimilation is insufficient for constraining column burdens in regions with complex vertical stratification. We propose that future operational systems integrate satellite-based vertical constraints to resolve the systematic winter bias identified here. Full article

Air pollution is an important environmental issue that affects social development and human life. Atmospheric fine particulate matter (PM_2.5) is the primary pollutant affecting the air quality of most cities in the authors’ country. It can cause severe haze, reduce air visibility and cleanliness, and affect people’s daily lives and health. Therefore, it has become a primary research object. Ground monitoring and satellite remote sensing are currently the main ways to obtain PM_2.5 data. Satellite remote sensing technology has the advantages of macro-scale, dynamic, and real-time functioning, which can make up for the limitations of the uneven distribution and high cost of ground monitoring stations. Therefore, it provides an effective means to establish a mathematical model—based on atmospheric aerosol optical thickness data obtained through satellite remote sensing and PM_2.5 concentration data measured by ground monitoring stations—in order to estimate the PM_2.5 concentration and temporal and spatial distribution. This study takes the Yangtze River Delta region as the research area. Based on the measured PM_2.5 concentration data obtained from 184 ground monitoring stations in 2023, the newly released sixth version of the MODIS aerosol optical depth product obtained via the US Terra and Aqua satellites is used as the main prediction factor. Dark-pixel AOD data with a 3 km resolution and dark-blue AOD data with a 10 km resolution are combined with the European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis meteorological, land use, road network, and population density data and other auxiliary prediction factors, and XGBoost and LSTM models are used to achieve high-precision estimation of the spatiotemporal changes in PM_2.5 concentration in the Yangtze River Delta region. Full article

Analyzing the spatiotemporal evolution and drivers of multifunctional territorial spatial utilization efficiency (TSE) is essential for guiding the sustainable use of territorial space. This study develops an evaluation system integrating urban, agricultural, and ecological spatial utilization, and investigates the Yangtze River Delta (YRD) from 2000 to 2023 using kernel density estimation and the XGBoost–SHAP model. The main findings are as follows: (1) TSE in the YRD exhibits a sustained upward trajectory and a distinct east–west gradient. At the sub-dimensional scale, urban spatial utilization efficiency is clustered in southeastern core cities, agricultural spatial utilization efficiency is concentrated in the central transition zone, and ecological spatial utilization efficiency is highest in the northern areas. (2) The overall regional disparity in multifunctional TSE shows a fluctuating yet declining trend, indicating a gradual reduction in spatial inequality. The inter-provincial imbalance in development is identified as the primary cause of spatial differentiation in the YRD. (3) Topography, economic density, and population density are the leading determinants of TSE, while their interactions with socioeconomic variables generate nonlinear effects on efficiency improvement. These conclusions provide empirical support for spatial planning and efficiency-oriented territorial governance in the YRD. Full article

This study conducted an intensive air quality model evaluation as a response to the urgent need to understand the reliability, consistency, and uncertainty of air quality models supporting the implementation of the PM_2.5 Air Pollution Control Action Plan in China. Five regional air quality models of CMAQ version 5.02, CMAQ version 5.3.2, CAMx version 6.2, CAMx version 7.1, and NAQPMS have been evaluated for the CO, SO₂, NO₂, O₃, PM₁₀, and PM_2.5 concentration and components. A unified statistical method and the same observational data set of 2017, comprising 17 air pollution episodes collected from four super monitoring stations in the regions of Beijing–Tianjin–Hebei, Yangtze River Delta, Pearl River Delta, and Chengdu–Chongqing in China, have been used for the evaluation. All the participating models performed well in simulating the mean PM_2.5 concentrations, with an NMB ranging from −0.29 to −0.04, showing that the participating models are basically suitable for simulation and as evaluation tools for PM_2.5 in regulatory applications. However, the participating models showed a great variability for PM_2.5 components, with the NME ranging from 0.48 to 0.53. The models performed reasonably well in simulating the mean sulfate, nitrate, BC, and NH₄⁺ concentration in PM_2.5, while they were diversified in simulating the mean OC concentrations. The participating models also consistently performed well in simulating the concentration of NO₂, CO, and O₃. However, the models generally overestimated SO₂ concentrations, and to some extent underestimated PM₁₀ concentrations, which is likely attributable to uncertainties in emission sources and the rapid implementation of strict control policies for SO₂. The evaluation work of this study shows that there remains significant potential for further enhancement. Updating and improving the emission inventory should be prioritized to achieve better results, and further investigations into the uncertainties associated with the meteorological simulations, chemical mechanisms, and physical parameterization options of air quality models should also be conducted in future work. Full article

Over the past decade, China’s industrialization and urbanization have accelerated rapidly, leading to the extensive consumption of fossil fuels and the accumulation of atmospheric pollutants, which pose significant health risks to the population. This study analyses the spatiotemporal evolution patterns of major air pollutants over the past decade based on data from meteorological- and environmental-factor monitoring from various observation stations in the Yangtze River Delta region of China from 2018 to 2024, as well as air pollution monitoring and statistical data such as mortality rates of weather-sensitive diseases and socioeconomic attributes of patients. Based on mathematical models, a quantitative ‘dose–response’ relationship is established among meteorological factors, air pollution factors and mortality rates of sensitive diseases within the region. (1) PM_2.5 and ozone are the primary air pollutants in the Yangtze River Delta region, with significant self-correlation characteristics in pollutants observed in coastal areas and regions around provincial capitals. (2) The synergistic effects of temperature + NO₂ and relative humidity + SO₂ significantly impact mortality from sensitive diseases, while the cumulative lag effect of relative humidity on respiratory diseases exhibits a V-shaped temporal variation. (3) Pollutant cumulative lag effects are pronounced, with a 10 μg/m³ increase in PM_2.5 leading to a 0.93% and 0.71% rise in the mortality risks of non-accidental and circulatory system diseases over the lag period of 15 days, compared to a single-day lag, showing an additional 0.06% and 0.04% increase, respectively. Full article