Search Results (14,970)

As a core component of power system reform, the transmission and distribution price reform plays a critical role in optimizing the grid regulation model and promoting efficient allocation of power resources by establishing an independent pricing mechanism based on “permitted cost plus reasonable return”. This study evaluates the provincial transmission and distribution price reform effect in China. First, an evaluation index system is constructed from four dimensions, namely, economic efficiency, security guarantee, market mechanism and social welfare. Second, a comprehensive evaluation model is developed using a multi-attribute decision-making model consist of the Best–Worst Method (BWM), entropy weight method (EWM) and cloud model. Of these, the BWM and EWM are employed to determine the indicator weights, and the cloud model is utilized to rank the transmission and distribution price reform effect. Third, an empirical assessment and analysis are conducted on three typical provinces in China. Empirical analysis reveals significant regional heterogeneity in reform effectiveness. Based on the comprehensive cloud expectation (Ex) values, Province B (eastern coastal) ranks first with an Ex of 82.10 (on a 0–100 scale), falling into the “good” grade; Province C (northern) ranks second with an Ex of 81.05, also “good”; and Province A (central-western) ranks third with an Ex of 78.70, likewise “good”. Province B’s leading position is attributed to synergistic outcomes in cost control, market vitality, and social welfare. The study can provide references for the sustainable development of electric power companies and the electricity industry. Full article

How AI can contribute to green sustainable development (GSD) in China is a critical yet underexplored question. Leveraging the staggered implementation of the National New Generation Artificial Intelligence Innovation and Development Pilot Zone (AIPZ) as a quasi-natural experiment, this study employs a difference-in-differences approach with panel data from 285 prefecture-level cities (2017–2022). The main findings are threefold. First, AI directly promotes GSD and, more importantly, indirectly enhances GSD by upgrading new-quality productivity (NQP)—a novel mechanism that distinguishes this study from conventional environmental policy evaluations. Second, the facilitating effect is not uniform: significant positive effects are detected in the western, eastern, and central regions, but not in the northeastern region; among major urban agglomerations, the Pearl River Delta, Chengdu-Chongqing, and Yangtze River Deltaexhibit significant effects, whereas the Middle Reaches of the Yangtze River and Beijing-Tianjin-Hebei region does not. Third, spatial spillover analysis reveals that AI’s favorable effect on GSD spreads primarily through intercity similarity in economic development level. These findings provide actionable insights for policymakers aiming to harness AI for sustainable development, highlighting the importance of fostering NQP and designing regionally differentiated strategies. Full article

As a key indicator of low-carbon economic transformation, the influencing factors of carbon productivity (CP) have attracted considerable academic attention. However, the study of the role of artificial intelligence (AI) technology innovation is comparatively confined. Using China’s prefecture-level-and-above cities as the sample, this study measures regional AI technology innovation based on AI patent stocks and empirically examines its impact on carbon productivity. The principal findings of this paper are as follows: (1) AI technology innovation boosts urban carbon productivity through three channels: enhancing green innovation, reducing transaction costs, and increasing AI attention. (2) The regional heterogeneity analysis shows that this positive impact of AI technology innovation on carbon productivity exerts a stronger facilitating effect on eastern regions, resource-dependent cities, and central cities. The heterogeneity analysis at the technological level further provides evidence of the effect of AI technology innovation on carbon productivity varying along different tiers of technological development, innovation mode, and innovation role. (3) The analysis identifies the energy structure as a pivotal threshold variable governing the efficacy of AI innovation in bolstering carbon productivity. Notably, crossing the threshold of clean energy penetration triggers an escalating positive feedback loop between AI innovation and carbon productivity. (4) Estimation of temporal effect dynamics via non-parametric panel model shows that the impact of AI technology innovation on CP exhibits phased characteristics. The coefficient became significantly positive in 2010 and peaked in 2015, after which its effect gradually weakened. This study provides comprehensive empirical evidence for understanding the relationship between AI technology innovation and CP and provides policy references for the use of AI technology to promote the coordinated achievement of economic growth and carbon reduction. Full article

As a strategic node of the Silk Road Economic Belt and a prototypical valley-type city, Lanzhou is subject to the dual constraints of rapid urbanization and an inherently fragile ecological foundation, making the coordination between urban expansion and ecosystem services a critical issue for regional sustainability. Drawing upon multi-temporal land use remote sensing datasets provided by the Chinese Academy of Sciences Resource and Environment Science Data Center, in conjunction with soil, meteorological, and socio-economic data, this study integrates a land use transition matrix, the InVEST model, a modified coupling coordination degree model, and the geographic detector to comprehensively examine land use dynamics, the spatiotemporal evolution of urban expansion, and the spatial heterogeneity of ecosystem services (i.e., carbon storage, water yield, habitat quality, and soil conservation) in Lanzhou. In addition, the coupling coordination relationship and its underlying driving mechanisms are systematically explored. The results demonstrate the following: (1) Between 1980 and 2020, urban land area in Lanzhou increased from 103.87 km² to 286.83 km², accounting for 2.17% of the total area, with cropland constituting the dominant source of expansion and exhibiting a fluctuating “high–low–high” conversion trajectory. (2) Ecosystem services exhibit pronounced spatial heterogeneity, with carbon storage and habitat quality displaying a pattern of “low in the southeast and high in the northwest”, water yield showing an increasing gradient from southeast to northwest, and soil conservation characterized by “lower values in central areas and higher values in peripheral regions”; (3) Urban expansion has accelerated significantly, with Yongdeng County and Gaolan County emerging as principal expansion hotspots during 2010–2020. (4) The dominant driving mechanism gradually shifted from natural factors to the synergistic interaction between natural and socioeconomic factors, and the interaction among driving factors markedly enhanced the explanatory power for ecosystem service evolution. (5) The coupling coordination degree has transitioned from widespread imbalance to a spatially differentiated pattern, characterized by relatively coordinated conditions in peripheral areas and persistent imbalance within the central urban core. These findings provide a robust scientific basis for territorial spatial optimization and the synergistic development of ecological and economic systems in valley-type cities, and offer important implications for sustainable development in arid and semi-arid regions. Full article

This study assesses export concentration risk in four Central Asian economies (Kazakhstan, Kyrgyzstan, Tajikistan, and Uzbekistan) by examining trade specialization patterns in 31 mineral, chemical, textile, and metallurgical product groups over 2017–2024. Using a multi-index framework based on Revealed Symmetric Comparative Advantage (RSCA), Relative Trade Advantage (RTA), and the Lafay Index (LI), the paper distinguishes structurally embedded competitive advantages from export signals that are weak, import-dependent, or potentially transient. The revised analysis adds explicit data consistency checks, a clarified classification rule, and robustness tests based on sign concordance, majority-index rules, and RSCA-only thresholds. The results show that Central Asia’s risk profile is highly persistent but heterogeneous: Tajikistan is exposed to extreme single-commodity risk in aluminium and cotton-related segments; Kazakhstan remains vulnerable to mineral-fuel concentration and energy-price volatility; Uzbekistan has broader but still labour-intensive textile specialization; and Kyrgyzstan shows ambiguous competitiveness that may partly reflect re-export and transit-related trade. Fully competitive product groups are confined mainly to resource- and labour-intensive activities, while chemicals and technologically complex manufacturing remain non-competitive across the region. The findings support risk-differentiated policy responses, including commodity-price hedging, counter-cyclical stabilization tools, downstream processing, textile upgrading, and regional value-chain development. Full article

Understanding how sustainable urban competitiveness evolves within megaregions has become a central concern in urban and regional studies, particularly under the pressures of carbon neutrality, spatial inequality, and network-driven urbanization. This study develops a multidimensional framework to assess the sustainable competitiveness of cities in the Beijing–Tianjin–Hebei “2 + 36” urban agglomeration and examines its spatio-temporal evolution and relational structure. Using a 30-indicator system grounded in factor foundations, economic performance, innovation capacity, openness, and environmental livability, we construct a composite competitiveness index through principal component analysis (PCA). Kernel density estimation reveals a pattern of overall improvement accompanied by widening disparities, characterized by selective agglomeration and the emergence of a pronounced high-value tail. Spatial autocorrelation consistently indicates significant spatial dependence, while LISA analysis identifies persistent low–low clusters and limited spillover absorption around core cities. A modified gravity model further uncovers a transition from a linear, corridor-based linkage structure to a more polycentric and networked competitiveness system, albeit with enduring peripheral weak nodes. The study contributes theoretically by conceptualizing sustainable urban competitiveness as a multidimensional nexus shaped jointly by territorial attributes and relational network structures. It demonstrates that competitiveness dynamics in megaregions emerge from the interplay of hierarchical consolidation, spatial divergence, and network reconfiguration—challenging the traditional assumption of simple core-to-periphery diffusion. The findings offer broader global implications, showing that the Beijing–Tianjin–Hebei case mirrors worldwide megaregional patterns, where proximity alone is insufficient to ensure functional integration, and where coordinated governance, network embeddedness and sustainability transitions increasingly determine regional competitiveness. This research provides a comprehensive analytical foundation for understanding and governing megaregional competitiveness in the era of sustainable development. Full article

To assess the potential risk of expansion of the sweet potato weevil (Cylas formicarius) in China under climate change, we combined principal component analysis in environmental space (PCA-env) with a Biomod2 ensemble model, using 173 occurrence records from its native range in India and its invaded range in China. We quantified the dynamics of the climatic niche between the native and invaded ranges and projected both current and future climatically suitable areas in China. Precipitation during the wettest month (Bio13), mean temperature during the driest quarter (Bio9), and isothermality (Bio3) were the key climatic predictors. Niche overlap between India and China was low (Schoener’s D = 0.107). The invaded niche was characterized by high stability (0.991) with very limited expansion (0.009), indicating strong niche conservatism. However, a relatively high unfilling value (0.633) suggests that the species has not yet occupied all potentially suitable climatic space in China. The current suitable area was estimated at 37.55 × 10⁴ km², primarily concentrated in South China and the southeastern coastal region. Under future climate scenarios, suitable habitat is projected to expand overall, extending into Central, Eastern, and Southwestern China. This study provides a climate-informed forecasting framework for assessing the potential spread of C. formicarius in China and offers practical support for quarantine surveillance and region-specific management. Full article

The Northern Yellow Sea Cold Water Mass (NYSCWM) is a distinctive hydrographic phenomenon in China’s coastal waters and is generally considered to originate from locally formed cold water during the previous winter. However, the specific wintertime period and local spatial range controlling its bottom-layer minimum temperature (BMT) remain unclear. This study utilizes August BMT data spanning 2003–2020, together with winter Multiscale Ultrahigh Resolution Sea Surface Temperature (MURSST) data. On this basis, K-means clustering is applied to identify the key spatiotemporal regions linked to BMT variability. Results show that the BMT of the NYSCWM exhibits a significant warming trend of about 0.0533 °C yr⁻¹ and a pronounced quasi-3-year oscillation. The strongest correlation (CC = 0.8396) between BMT and winter SST occurs in the central Northern Yellow Sea (NYS) during the second half of February, exceeding that in other regions. This area acts as a key spatiotemporal region, located between colder western waters and warmer southern sectors, and maintains persistently low temperatures during this period. A regression model based on SST in this key spatiotemporal region reproduces observed BMT with a correlation coefficient of 0.9146 and enables prediction six months in advance. These results refine the identification of key spatiotemporal regions and improve our understanding of NYSCWM formation and evolution. Full article

Despite substantial improvement in air quality in China, winter PM_2.5 concentrations particularly in January show limited decline, especially in the central region. This study used statistical analysis and WRF-CMAQ to examine how typical meteorological years affect transport and pollution processes in Henan. The mean effect difference ranged from −22 to 33%. In January 2020, weak winds and a low planetary boundary layer increased PM_2.5 by 3–33%, whereas in January 2023, stronger northerly winds and a higher boundary layer reduced PM_2.5 by 12–22%. These differences altered transport pathways, leading to a shift in dominant source regions from Beijing–Tianjin–Hebei and Shandong to Anhui and Hubei, with primary PM_2.5 showing high sensitivity to transport pathways, whereas secondary PM_2.5 remained relatively stable due to its dependence on regional chemical formation. Typical meteorological years in Henan exhibit two distinct pollution regimes: The local accumulation regime (2020) showed faster growth (20–30 μg m⁻³ d⁻¹), a higher peak (107 μg m⁻³), longer persistence, and slower dissipation and was dominated by near-range transport. In contrast, the regional transport regime (2023) exhibited slower growth (<20 μg m⁻³ d⁻¹), a lower peak (99 μg m⁻³), shorter persistence, and more rapid dissipation and was sustained by multi-regional input from Anhui, Shandong, and Hubei. In both episodes, primary PM_2.5 dominated during the growth and peak stages, whereas secondary PM_2.5 played a more prominent role during dissipation. Full article

Revealing the spatial differentiation patterns of cultivated land multifunctionality contributes to the improvement of cultivated land protection policies. This study investigated the spatiotemporal differentiation characteristics and functional zoning of cultivated land multifunctionality in Alpine Valley Area from a topographic gradient perspective. An evaluation index system for cultivated land multifunctionality in Alpine Valley Area was constructed across four dimensions: production (PF), social (SF), ecological (EF), and landscape (LF) functions. Using Yulong County, Yunnan Province, as a case study, methods including kernel density analysis, standard deviation ellipse theory, topographic gradient analysis, and hierarchical clustering were employed to quantify the horizontal and topographic gradient characteristics of the multifunctionality of cultivated land from 2010 to 2020, thereby delineating functional zones. Results indicated: (1) Cultivated land multifunctionality shows clear topographically-dependent spatial differentiation: PF concentrates in central basins and northwest specialty agricultural zones, SF overlaps with production but with more dispersed high/low values, EF follows a “high in the center, low on the lateral areas” pattern, and LF remains relatively stable; (2) Significant hierarchical differences in cultivated land functions were observed along the elevation, slope, and terrain niche index (TNI) gradients. PF, EF, and LF generally decreased with increasing elevation, slope, and TNI, whereas the dominance of SF exhibited an inverted-V-shaped distribution along the gradient. (3) The study area was divided into five zones: Flat-Basin Agritourism Zone (FAZ), River-Valley Eco-Agriculture Zone (REZ), Sub-Alpine Specialty Agricultural Production Zone (SSAPZ), Sub-Alpine Steep Slope Integrated Management Zone (SSIMZ), and Mid-Mountain Steep Slope Ecological Conservation Zone (MSECZ), with differentiated strategies proposed for each. This study innovatively integrates a topographic gradient perspective, TNI, and hierarchical clustering to systematically evaluate the cultivated land multifunctionality in Alpine Valley Area, providing a new methodological framework for similar mountainous regions. Full article

Existing research on the landscape patterns of urban parks and green spaces demonstrates a disproportionate focus across tiers within China’s urban hierarchy. Numerous studies have concentrated on economically developed first-tier cities, such as Beijing, Shanghai, and Guangzhou. In contrast, medium-to-large non-first-tier cities, especially provincial capitals and emerging cities within the first- and second tiers, have been relatively understudied, although they have received increasing attention in recent years. This bias extends regionally, with studies predominantly examining cities in the more developed central and eastern regions, while less-developed areas and lower-tier cities receive significantly less attention. This study tracks changes in park quantity, spatial concentration, patch structure and driver associations at three planning-related time points. Shenyang provides a distinct cold-region and old industrial city case, shaped by long winters, industrial renewal and outward urban growth. Furthermore, to inform park and green-space planning in Northeast China’s cold-climate cities, exemplified here by Shenyang, a major metropolis with a monsoon-influenced humid continental climate (Köppen Dwa), long cold winters, and relatively short warm summers, we document a shift in park distribution from the urban core to peripheral areas. Based on park vector layers reconstructed from planning documents, remote sensing interpretation and field verification, this study combined spatial analysis, landscape metric calculation and driver-association modeling. ArcGIS Pro was used to identify changes in distribution centers, directional extension and local clustering; FRAGSTATS 4.2 was used to calculate park landscape metrics; and SIMCA-P 14.1 was used to examine the statistical associations between selected landscape indicators and potential driving variables. The results show that the number and total area of parks in central Shenyang increased substantially from 2000 to 2024. Spatially, park distribution became less concentrated in the traditional inner city, while new clusters gradually appeared in peripheral districts and newly developed urban areas. The old urban core remained important, but its dominance weakened as park provision expanded outward. The landscape metric results further indicate that park expansion was accompanied by more irregular patch forms, stronger fragmentation and declining structural continuity. The driver association analysis suggests that climate conditions, population change, industrial restructuring, real estate investment, road construction and urban greening policies were related to different aspects of park landscape change. These associations should be interpreted as statistical relationships rather than direct causal effects. Overall, this study clarifies the spatial restructuring of park green spaces in a cold-region old industrial city and provides planning evidence for improving park connectivity, coordinating green space expansion with urban construction and supporting sustainable park system development in Northeast China. Full article

Within the trans-regional ecological governance framework of the Yangtze River Delta (YRD), Anhui Province acts as a critical ecological barrier. Yet, intra-provincial disparities in the fiscal pressure of ecological compensation remain underexplored. Drawing on panel data from 16 prefecture-level cities in Anhui (2018–2024), we develop a hierarchy–region dual-dimensional framework. This framework measures fiscal pressure by integrating cost–benefit and opportunity–cost methods. A two-way fixed-effects model exhibits a distinct spatial gradient: fiscal pressure decreases in the order of Southern (19.58%) > Northern (13.45%) > Central Anhui (5.24%). Mechanism tests support the “Triple Systemic Mismatch” as a coherent interpretive lens: fiscal pressure is positively associated with ecological contribution and negatively associated with fiscal capacity and industrial structure. Furthermore, regional integration policy significantly alleviates such fiscal pressure. Accordingly, this study puts forward a three-dimensional sharing mechanism that integrates government coordination, market empowerment, and social participation to support equitable cross-regional governance. Full article

Background/Objectives: Multidrug-resistant tuberculosis (MDR-TB) remains a major public health challenge in the WHO European Region, which reports the highest global proportion of rifampicin-resistant and MDR-TB cases. Whole-genome sequencing (WGS) has emerged as a key tool for improving drug-resistance detection and supporting molecular surveillance. However, the level of genomic implementation across Central and Eastern Europe (CEE) remains insufficiently characterized. This scoping review aimed to evaluate the use of WGS for MDR-TB in CEE countries and to classify implementation maturity using a predefined framework (L0–L4). Methods: A structured search of PubMed/MEDLINE and Web of Science identified original studies published in English between 2015 and 2026 reporting genomic applications in MDR-TB across 13 predefined CEE countries. Data were extracted on sequencing approaches, resistance prediction, transmission analysis, monitoring of new or repurposed drugs, bioinformatic pipelines, and programmatic integration. Countries were categorized according to a five-level maturity model based on documented capacity, scope of application, and integration into national tuberculosis programs (NTPs). Results: Twenty-eight studies were included. WGS was used in 23/28 studies (82.1%), predominantly for genomic resistance prediction (25/28). Transmission analysis was reported in 19/28 studies, with heterogeneous single nucleotide polymorphism (SNP) thresholds and clustering methodologies. Monitoring of resistance to new or repurposed drugs was described in 8/28 studies. No country achieved Level L4 (formally integrated genomic surveillance). Four countries were classified as L3 and nine as L2, while no L0 or L1 settings were identified. Conclusions: Countries in Central and Eastern Europe demonstrate increasing operational use of WGS for MDR-TB, primarily driven by clinical resistance prediction. However, the lack of formal integration into national surveillance systems highlights a persistent gap between technological adoption and structured public health implementation. Strengthening programmatic integration and methodological standardization is essential for advancing genomic surveillance of MDR-TB in the region. Full article

A modified Pitot-tube jet (PTJ) separation pump combines centrifugal phase separation with pressure buildup and enables compact oil–water treatment, where a water-rich stream can be discharged at elevated pressure. This work advances an existing laboratory PTJ configuration toward a turbomachinery-oriented rotor concept for systematic design studies and subsequent field-oriented prototypes. Starting from a centrifuge-like reference configuration without blades that prioritizes separation stability, an impeller with trimmed blades is introduced to increase pressure head while limiting blade interaction with the oil–water interface by operating primarily in the outer, water-rich annulus. Comparative experiments with and without the impeller show a pronounced increase in pressure head, up to about a factor of three at the maximum speed investigated. The results also indicate a purity penalty caused by blade-induced mixing and secondary flows. This exposes the central design trade-off of the PTJ machine. Higher specific work input increases pressure head but can reduce discharge quality. Hydraulic optimization, therefore, needs to be coupled to ppm-level purity constraints. Density-based monitoring lacks resolution in the relevant trace range, and chemical-based analyses are too slow for systematic investigations. An imaging-based fluorescence method using Nile Red as a selective tracer is, therefore, implemented as a rapid analysis tool. High-resolution imaging with automated region of interest evaluation provides a robust calibration from 5–500 ppm for safe, non-fluorescent model oils such as sunflower oil. This enables efficient operating-window mapping and comparative screening of rotor concepts under reproducible conditions. Full article

In the context of the rapid growth of the digital economy and the continued implementation of China’s “dual carbon” strategy, clarifying the interactive relationships among the digital economy, low-carbon logistics, and the ecological environment is crucial for promoting sustainable regional development and green transformation. Based on the theoretical mechanisms underlying the coordinated development of these three systems, this study constructs a comprehensive evaluation index system for the Digital Economy–Low-Carbon Logistics–Ecological Environment (DLE) system. The entropy weighting method, a modified coupling coordination model, kernel density estimation, spatial autocorrelation analysis, and the barrier model are integrated to investigate the spatiotemporal evolution and driving mechanisms of coupling coordination among the three systems. The results indicate that (1) the development levels of the digital economy, low-carbon logistics, and the ecological environment have generally increased, although their evolutionary trajectories differ across stages. The digital economy shows the most rapid improvement, low-carbon logistics maintains steady progress, and the ecological environment exhibits gradual optimization. (2) From a temporal perspective, the overall coupling coordination of the national DLE system has shown a fluctuating upward trend, with the coordination type gradually evolving from a near-coordination stage to an initial coordination stage, though it remains at a low-to-medium coordination level overall. (3) From a spatial perspective, the coupling coordination degree presents a stable gradient pattern, with higher levels in eastern China, intermediate levels in central China, and lower levels in western China. Medium- and high-coordination areas are gradually extending from coastal regions to inland areas, while regional disparities remain evident. (4) The spatial autocorrelation results reveal significant positive spatial clustering at the provincial level. Both high-value and low-value clusters show a certain degree of stability, indicating clear spatial spillover effects. (5) An analysis of constraining factors reveals that insufficient scale of digital economic development and innovation application capabilities, constraints on ecological and environmental resource carrying capacity and governance, as well as low operational efficiency and delayed transformation of low-carbon logistics, are the primary types of obstacles hindering the coordinated improvement of the three systems. These findings provide empirical evidence and policy implications for leveraging the digital economy to facilitate low-carbon logistics transformation and enhance coordinated regional sustainability. Full article